Graph Construction - dbdmg.polito.it
Transcript of Graph Construction - dbdmg.polito.it
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Graph Construction
Data Management and Visualization
Version 2.6.0© Marco Torchiano, 2020
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Licensing Note
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▪ Any of these conditions can be waived if you get permission from the copyright holder.
Your fair use and other rights are in no way affected by the above.
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Grammar of Graphics
▪ Theory behind graphics construction
Separation of data from aesthetic
Definition of common plot/chart elements
Composition of such common elements
▪ Building a graphic involves
1. Specification
2. Assembly
3. Display
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Leland Wilkinson, The grammar of graphics
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Specification
▪ DATA: a set of data operations that create variables from datasets
Link variables (e.g., by index or id)
▪ TRANS: variable transformations (e.g., rank)
▪ SCALE: scale transformations (e.g., log)
▪ COORD: a coordinate system (e.g., polar)
▪ ELEMENT: visual objects (e.g., points) and their aesthetic attributes (e.g., color, position)
▪ GUIDE: guides (e.g., axes, legends)
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Specification for a scatter plot
▪ DATA: x = x
▪ DATA: y = y
▪ TRANS: x = x
▪ TRANS: y = y
▪ SCALE: linear(dim(1))
▪ SCALE: linear(dim(2))
▪ COORD: rect(dim(1, 2))
▪ GUIDE: axis(dim(1))
▪ GUIDE: axis(dim(2))
▪ ELEMENT: point(position(x*y))
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Graph visual components
▪ Data components
Visual objects associated to measures
Visual attributes
▪ Layout
Positioning rules (e.g. cartesian coord)
▪ Support components
Axes
Labels
Legends
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Visual Encoding
▪ Given a variable (measure), identify:
Visual object
Visual attribute
▪ Main distinction
Quantitative (interval, ratio, absolute)
Categorical (nominal, ordinal)
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VISUAL RELATIONSHIPS
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Data Visualization
Visual PerceptionVisual Properties & Objects
Quantitative ReasoningQuantitative Relationship & Comparison
Information VisualizationVisual Patterns, Trends, Exceptions
Understanding
Data
Representation/Encoding
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Relationships
▪ Within a category
Nominal comparison
Ranking
Part-to-whole
Distribution
▪ Between measures
Time series
Deviation
Correlation
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Quantitative encoding
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Nominal comparison
▪ Compare quantitative values corresponding to categorical levels
Small differences are difficult to see
– Non zero-based scale can emphasize
Dot plots can be used for small differences
– They do not require zero based scale
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Line length - Bars chart
0 20 40 60 80 100
Large
Medium
Small
Micro
Number of companies
Size
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Vertical Bars (aka Columns)
0
20
40
60
80
100
Large Medium Small Micro
Num
ber
of
com
panie
s
Size of the company
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Bar charts
▪ Categorical values are encoded as position along an axis
▪ Quantitative values are encoded only as length of the bars
The axis is a supporting element
▪ Width of bars plays no role
Bars are just very thick lines
▪ Bars require a zero-based scale
See: Lie factor!
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Comparison - Barplot
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Barplot (non zero based scale)
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Barplot (non zero based scale)
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Proportionality:
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Barplot vertical labels
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Bars Guidelines
▪ Use horizontal bars when
A descending order ranking
Categorical label don’t fit
▪ Proximity
Use a 1:1 bar:spacing ratio ±50%
No spacing between bars that are not labeled on the axis (legend categories)
No overlapping bars
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Position - Dots plot
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0 20 40 60 80 100
Large
Medium
Small
Micro
Size
Number of Companies
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Dot plots
▪ Categorical values are encoded as position along an axis
▪ Quantitative values are encoded as position along an axis
There is no need to have a zero based axis range
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Comparison – Dot plot
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Area - Bubble plot
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Extremely difficult to compare size
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Count - Isotype
▪ Isotype
International System Of Typographic Picture Education
▪ Marie and Otto Neurath
Vienna, 1936
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Ranking
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▪ Same type as nominal comparison
▪ Pay attention to order
Bar graphs
Dot plot
– Allow non zero-based axes
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Ranking
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Purpose Sort order Chart orientation
Highlight the highest value
DescendingH: highest on topV: highest on left
Highlight the lowest value
AscendingH: lowest on topV: lowest on left
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Ranking - Barplot
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Ranking – Dot plot
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Lollypop (non zero based scale)
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Lollypop (zero based scale)
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Deviation
▪ To what degree one or more sets of values differ in relation to primary values.
Points (dots)
Gauge
Bars
Bullet
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Angle + Position - Gauge
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Satisfaction
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Length+Position- Bullet Graph
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https://www.perceptualedge.com/articles/misc/Bullet_Graph_Design_Spec.pdf
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Pre-post variation
▪ Comparing several categorical values typically two conditions
Pre vs. post
With vs. without
…
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Slope chart
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Dumbbell plot
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Clustered bars
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Proportion (Part-to-whole)
▪ Best unit: percentage
▪ Stacked bar graph
Difficult to read individual values
▪ Stacked area
▪ Treemap
▪ Gridplot
▪ Pie / Donut
▪ Marimekko
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Length – Stacked Bar
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Beware MS-Excel Default
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98%
99%
99%
99%
99%
99%
100%
100%
100%
100%
1
NO
YES
A B
1 YES 99%
2 NO 1%
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Stacked bar graph
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?
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Stacked bars w/percentage
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Area - Treemap
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Area - Treemap
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Area + Count – Waffle / Grid
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Area + Angle – Pie Chart
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Pies
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Pies vs. Bars
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Pie Charts: guidelines
▪ Have serious limitations
To represent part-whole relationship
Only with a small number of categories
– Up to four
– Avoid rainbow pie
When proportions are distinct enough
▪ Remember to ease reading
Labels placed close to slices
Labels include values (percentages)
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Area/Angle/Length – Donut
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Pareto chart
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Marimekko Chart
53https://www.fusioncharts.com/chart-primers/marimekko-chart/
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Distribution
▪ Two main types
Show distribution of single set of values
Show and compare two or more distributions
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Single distribution
▪ Histogram Vertical bar graph
Frequency for subdivision– Quantitative ranges
– Categories
Emphasis on number of occurrences
▪ Frequency polygon Line graphs
Frequency density function
Emphasis on the shape of the distribution
▪ Boxplot Summary
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Histogram
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Frequency polygon
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Boxplot
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▪ Max value
▪ 3rd quartile
▪ Median
2nd quartile
▪ 1st quartile
▪ Min value
▪ Outlier
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Violin plot
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▪ Max value
▪ Frequency polygon
mirrored
▪ Min value
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Violin + Boxplot
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▪ Overlaying a box plot over the violin provides additional details
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Multiple distribution
▪ Histogram is not suitable
▪ Frequency polygon
Line graphs
Frequency density function
▪ Boxplot
Summary
Less distracting with high number of categories
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Paired diverging bargraph
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https://unstats.un.org/unsd/genderstatmanual/Print.aspx?Page=Presentation-of-gender-statistics-in-graphs
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Multiple Frequency polygons
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Multiple Box plot
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Violin plot
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Multiple box plots
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Multiple violin plots
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Confidence Intervals
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Error Bars Considered Harmful: Exploring Alternate Encodings for Mean and Error Michael Correll, and Michael GleicherIEEE Transactions on Visualization and Computer Graphics, Dec. 2014
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Interval may be Asymmetric
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It is physically impossible to
modify -6 files
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Likert / Agreement
▪ Likert scale:
Measures agreement / disagreement with a given statement
Response on an ordinal scale, e.g.– Definitely No
– Mostly No
– Undecided
– Mostly Yes
– Definitely Yes
▪ Often used to measure positive vs. negative perception
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Diverging stacked bars
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Macroarea N° Domanda
1Il carico di studio complessivo degli insegnamenti previsti nel
periodo didattico è accettabile?
2L'orario degli insegnamenti del periodo didattico è ben
organizzato?
3Le regole d'esame, gli obiettivi e il programma
dell'insegnamento sono stati resi noti in modo chiaro?
4L'insegnamento è stato svolto in maniera coerente con quanto
dichiarato sul portale della didattica?
5Le conoscenze preliminari da me possedute sono risultate
sufficienti per la comprensione della materia ?
6Il carico di studio richiesto da questo insegnamento è
proporzionato ai crediti assegnati?
7Il materiale didattico, indicato o fornito, è adeguato per lo
studio della materia?
8Le attività didattiche integrative (esercitazioni, lab, seminari,
visite, ecc.) sono utili per l'apprendimento della materia?
9Il docente rispetta gli orari di svolgimento dell'attività
didattica?
10 Il docente è disponibile a fornire chiarimenti e spiegazioni?
11Il docente interagisce efficacemente con gli studenti,
stimolando l'interesse verso la materia?
12 Il docente espone gli argomenti in modo chiaro?
13 Le aule in cui si svolgono le lezioni sono adeguate?
14I locali e le attrezzature per le attività didattiche integrative
sono adeguati?
15Sono interessato agli argomenti di questo insegnamento?
(indipendentemente da come è stato svolto)
16 Sono soddisfatto di come è stato svolto questo insegnamento?
17Al fine dell apprendimento, la frequenza alle attività didattiche
è utile?
Organizzazione del
periodo didattico
Organizzazione di
questo insegnamento
Efficacia del docente
Infrastrutture
Interesse e
soddisfazione
-50% 0% 50% 100%
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
D11
D12
D13
D14
-50% 0% 50% 100%
D15
D16
D17
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Time series
▪ Series of relationships between quantitative values that are associated with categorical subdivisions of time
▪ Communicate
Change
Rise
Increase
Fluctuate
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Grow
Decline
Decrease
Trend
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Time series
▪ Time grows from left to right
Cultural convention
▪ Vertical bars
highlight individual points in time
hide overall trend
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Line plot
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Bars
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Streamgraph
76http://www.neoformix.com/2008/TwitterTopicStream.html
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Correlation
▪ Relationships between two paired sets of quantitative values
Scatter plot w/possible trend line
– Ok for educated audience
Paired bar graph
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Points
0
1
2
3
4
5
6
7
8
9
10
0 5 10 15 20 25
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Points Guidelines
▪ Points must be clearly distinguished
Enlarge points
Select radically distinct shapes (✚)
Balance size of points and graph
Use outlined shapes
▪ Lines must not obscure points
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Scatter plot
80
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Overplotting
▪ Phenomenon related to multiple points (or shapes) overlapping
Discrete (integer) measure
Very large dataset
▪ Solutions
Small shapes
Outlined shapes
Transparent shapes (alpha)
Jittering
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Overplotting example
82
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Overplotting - Small
83
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Overplotting - Outlined
84
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Overplotting - Transparent
85
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Overplotting - Jittering
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Points and Lines
0
500
1000
1500
2000
2500
3000
3500
4000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
87
The slope encodes the amount of change.Warning: non linear!
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Slope of lines
0
500
1000
1500
2000
2500
3000
3500
4000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
88
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Slope of lines
0
1
2
3
4
5
6
7
8
9
10
0 5 10 15 20 25
89
Trend lineLine of best fitThe slope encodes the regression coefficient
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Lines
▪ Easy perception of trends and overall shape of data
▪ Best suited for time series
▪ Variation encoded as slope
Clear direction
Approximate magnitude
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Paired diverging bars
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Categorical encoding attributes
▪ Encoding of categorical levels
Position (along an axis)
Size
Color– Intensity
– Saturation
– Hue
Shape
Fill pattern
Line style
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Ordinal
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Position
0
10
20
30
40
50
60
70
80
90
Large Medium Small Micro
Number of
companies
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Color (hue)
0
100.000
200.000
300.000
400.000
500.000
600.000
Q1 Q2 Q3 Q4
2003 Sales
Direct Indirect
94
Position ×
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Size
95
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Point shape
600.000
650.000
700.000
750.000
800.000
850.000
Q1 Q2 Q3 Q4
Booking Billing
96
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Line style
600.000
650.000
700.000
750.000
800.000
850.000
Q1 Q2 Q3 Q4
Booking
Billing
97
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Fill Texture
98
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Discretization / Quantization
▪ A data transformation that maps a quantitative measure into an ordinal one
Based on the definition of intervals
▪ Discretized measures can be encoded using an ordinal-friendly visual attribute
Size
Color
▪ Warning: details are lost in the process
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Heatmaps
100http://graphics.wsj.com/infectious-diseases-and-vaccines/
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Heatmaps
▪ Hues have no unique order semantics
Only intensity has one
▪ Rainbow palette have serious problems for color blinds
Roughly 5% of the population
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Heatmaps
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SUPPORT ELEMENTS
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Support elements
▪ Axes
Ticks
▪ Graph area
Grids
▪ Labels
▪ Legends
▪ References
▪ Trellies
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Axes
▪ Allow positioning of elements
Points
Extremes of bars and lines
▪ Labeled
What is the measure?
▪ Number of axis should be 2
1 is fine for bars
– continuity gestalt principle
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Tick marks
▪ Must not obscure data objects
▪ Outside the data region
▪ Avoid for categorical scales
▪ Balanced number
Too many clutter the graph
Too few make difficult to discern reference for data objects
Intervals must be equally spaced
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Multiple variables
▪ Correlation between 3+ variables
E.g. two measures in time series
▪ Multiple units of measure
Double quantitative (y) axis
Multiple graphs
One variable not encoded explicitly
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Double scale
108
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Double scale (alternative)
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Multiple graphs
110
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Path
111
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Small multiples
▪ A.k.a.
Trellis
Lattice
Grid
▪ Set of aligned graphs sharing (at least one) scale and axis
Enable ease of comparison among different measures
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Small multiples
113
FT EU unemployment trackerhttp://blogs.ft.com/ftdata/2015/04/17/eu-unemployment-tracker/
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Trellis
▪ Sequence
Intrinsic order
Order of relevance
Order by some quantitative attribute
▪ Rules and grids
Use when spacing is not enough
Can direct the reader to scan graphs horizontally or vertically
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Log scale
▪ Reduce visual difference between quantitative data sets with significantly wide ranges
▪ Differences are proportional to percentages
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Log scale
116
100
180
324
0
100
200
300
400
A B C
100180
324
1
10
100
1000
A B C
+80
+144
+80%+80%
Same absolute gainscorrespond to same distance
Same percentage gains correspond to same distance
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Log scale
117
0
20000
40000
60000
80000
100000
120000
140000
Q1 Q2 Q3 Q4
North
South
1
10
100
1000
10000
100000
1000000
Q1 Q2 Q3 Q4
North
South
Parallel lines for same absolute gains
Parallel lines for same percentage gains
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Graph area
▪ Aspect ratio should not distort perception
Typically wider than taller
Scatter plots may be squared
▪ Grid lines must be thin and light
Useful to look-up values
Enhance comparison of values
Enhance perception of localized patterns
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Labels
▪ Important elements (e.g. titles) should be prominent
Top
Larger
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Guthenberg Diagram
120
Primary area Strong fallow area
Weak fallow area Terminal area
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Guthenberg Diagram
121
Primary area Strong fallow area
Weak fallow area Terminal area
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Legends
▪ Used for categorical attributes not associated to any axis
▪ As close as possible to the objects
▪ Less prominent than data objects
▪ Borders are used only when necessary to separate from other elements
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Legends
▪ Text should be as close as possible to the object it complements
Prefer direct labeling to separate legends
▪ Number of categorical subdivisions
Perceptual limit is between 5 and 8
Limit is independent of the visual attribute used to encode it
Joint use of attributes ease discrimination
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Legend
600.000
650.000
700.000
750.000
800.000
850.000
Q1 Q2 Q3 Q4
Booking
Billing
124
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Direct labeling
600.000
650.000
700.000
750.000
800.000
850.000
Q1 Q2 Q3 Q4
Booking
Billing
125
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Direct labeling and color
600.000
650.000
700.000
750.000
800.000
850.000
Q1 Q2 Q3 Q4
Booking
Billing
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Legend
0 100 200 300 400 500 600
Q1
Q2
Q3
Q4
Migliaia
2003 Sales Indirect
Direct
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Direct labeling
0 100 200 300 400 500 600
Q1
Q2
Q3
Q4
Migliaia
2003 SalesIndirect
Direct
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Reference lines and regions
▪ Reference lines support an easy comparison to a given value
Mean
Threshold
▪ Reference regions allow comparison with several values
Use background color
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DASHBOARD
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Dashboard
Visualization of the most relevantinformation
needed to achieve one or more goals
which fits entirely on a single screen so it can be monitored at a glance
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Dashboard
▪ Dashboards display mechanisms are
small
concise
clear
intuitive
▪ Dashboards are customized
To suit the goals of person, group, function
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Provide context for data
▪ References allow judging the data
133
Figure 3‐3. This dashboard demonstrates the effectiveness that is sacrificed when scrolling is required to see all the information.
3.2. Supplying Inadequate Context for the Data Measures of what's currently going on in the business rarely do well as a solo act; they need a good
supporting cast to succeed. For example, to state that quarter‐to‐date sales total $736,502 without any
context means little. Compared to what? Is this good or bad? How good or bad? Are we on track? Are we
doing better than we have in the past, or worse than we've forecasted? Supplying the right context for key
measures makes the difference between numbers that just sit there on the screen and those that enlighten
and inspire action.
The gauges in Figure 3‐4 could easily have incorporated useful context, but they fall short of their potential.
For instance, the center gauge tells us only that 7,822 units have sold this year to date, and that this
number is good (indicated by the green arrow). A quantitative scale on a graph, such as the radial scales of
tick marks on these gauges, is meant to provide an approximation of the measure, but it can only do so if
the scale is labeled with numbers, which these gauges lack. If the numbers had been present, the positions
of the arrows might have been meaningful, but here the presence of the tick marks along a radial axis
suggests useful information that hasn't actually been included.
Figure 3‐4. These dashboard gauges fail to provide adequate context to make the measures meaningful.
These gauges use up a great deal of space to tell us nothing whatsoever. The same information could have
been communicated simply as text in much less space, without any loss of meaning:
Table 3‐1.
YTD Units 7,822
October Units 869
Returns Rate 0.26%
Another failure of these gauges is that they tease us by coloring the arrows to indicate good or bad
performance, without telling us how good or bad it is. They could easily have done this by labeling the
quantitative scales and visually encoding sections along the scales as good or bad, rather than just encoding
the arrows in this manner. Had this been done, we would be able to see at a glance how good or bad a
measure is by how far the arrow points into the good or bad ranges.
The gauge that appears in Figure 3‐5 does a better job of incorporating context in the form of meaningful
comparisons. Here, the potential of the graphical display is more fully realized. The gauge measures the
average duration of phone calls and is part of a larger dashboard of call‐center data.
Supplying context for measures need not always involve a choice of the single best comparisonrather,
several contexts may be given. For instance, quarter‐to‐date sales of $736,502 might benefit from
comparisons to the budget target of $1,000,000; sales on this day last year of $856,923; and a time‐series
of sales figures for the last six quarters. Such a display would provide much richer insight than a simple
display of the current sales figure, with or without an indication of whether it's "good" or "bad." You must
be careful, however, when incorporating rich context such as this to do so in a way that doesn't force the
viewer to get bogged down in reading the details to get the basic message. It is useful to provide a visually
prominent display of the primary information and to subdue the supporting context somewhat, so that it
doesn't get in the way when the dashboard is being quickly scanned for key points.
Figure 3‐5. This dashboard gauge (found in a paper entitled "Making Dashboards Actionable," written by Laurie M. Orlov and published in December 2003 by Forrester Research, Inc.) does a better job than those in Figure 3‐4 of using a gauge effectively.
PUC
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Use appropriate detail
▪ Typical counter-examples
Dates with seconds detail
Decimals
134
136,0
PUC
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Use the right measures
▪ If you are interested in e.g. the difference, ratio, variation show such derived measure
135
-0,25
0,00
0,25
0,50
0,75
1,00
1,25
1,50
2015 2016 2017 2018 2019 2020
Variazione Debito Pubblico (% PIL)
PD M5S-L
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Use appropriate visualization
▪ Typical errors:
Any chart when a table would be better
Pie-charts not representing part-whole
Bubble charts
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Visualization instruments
▪ Tables
Textual information
▪ Graphs
Visual information
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Avoid decorations
▪ Skeumorphic design
▪ Backgrounds motives
▪ Color gradients
▪ Variations not encoding any measure
Typically color
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Avoid decorations
▪ Skeumorphic design
▪ Backgrounds motives
▪ Color gradients
▪ Variations not encoding any measure
Typically color
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Avoid decorations
▪ Skeumorphic design
▪ Backgrounds motives
▪ Color gradients
▪ Variations not encoding any measure
Typically color
140
A
B
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Avoid decorations
▪ Skeumorphic design
▪ Backgrounds motives
▪ Color gradients
▪ Variations not encoding any measure
Typically color
141
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3D diagrams
▪ Encoding
Axonometry typically hides some data and makes comparison hard
▪ Not encoding
Perspective deform dimensions
Depth or height distract and make comparison more difficult
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Encoding 3D
143
Ricerca
Vendite
Gestione
Contabilità
0
10
20
30
40
50
60
70
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Encoding 3D → 2D
144
0 20 40 60 80
Paghe
Attrezzature
Viaggi
Consumabili
Software
Altro
Ricerca
0 20 40 60 80
Vendite
0 20 40 60 80
Gestione
0 20 40 60 80
Contabilità
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Decorative 3D
145
0
100000
200000
300000
400000
500000
600000
700000
2015 20142013
20122011
20102009
20082007
Immatricol.
FIAT
WV
Ford
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Decorative 3D → 2D
146
FIAT
VW
Ford
0
100
200
300
400
500
600
2007 2009 2011 2013 2015
Immatricol.
Mig
liaia
Anno
Immatricolazioni auto per marchio
sul mercato italiano
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References
▪ Stephen Few, 2004. Show me the numbers. Analytics Press.
http://www.perceptualedge.com/blog/
▪ Edward R. Tufte, 1983. The Visual Display of Quantitative Information. Graphics Press.
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References
▪ Wilkinson, L. (2006). The grammar of graphics. Springer Science & Business Media.
▪ Wickham, H. (2010). A layered grammar of graphics. Journal of Computational and Graphical Statistics, 19(1), 3-28.
▪ Visual Vocabulary http://ft.com/vocabulary
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References
▪ R.Olson. Revisiting the vaccine visualization
http://www.randalolson.com/2016/03/04/revisiting-the-vaccine-visualizations/
▪ Nathan Yau. 9 Ways to Visualize Proportions – A Guide
http://flowingdata.com/2009/11/25/9-ways-to-visualize-proportions-a-guide/
▪ M.Correll, and M.Gleicher. Error Bars Considered Harmful: Exploring Alternate Encodings for Mean and Error IEEE Transactions on Visualization and Computer Graphics, Dec. 2014 http://graphics.cs.wisc.edu/Papers/2014/CG14/Prep
rint.pdf
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