Links

Tableau License for Students

GitHub Student Pack

Block Builder

data

Administrative

a0, a1 due

1X Y

2X Y

3X Y

4X Y

MeanVariance

Correlation

Anscombe’s Quartet: Raw Data

1X Y

2X Y

3X Y

4X Y

MeanVariance

Correlation

Anscombe’s Quartet: Raw Data

1X Y

2X Y

3X Y

4X Y

MeanVariance

Correlation

Anscombe’s Quartet: Raw Data

1X Y

2X Y

3X Y

4X Y

MeanVariance

Correlation

Anscombe’s Quartet: Raw Data

1X Y

2X Y

3X Y

4X Y

MeanVariance

Correlation

Anscombe’s Quartet: Raw Data

Tableau

typical vis design

Key question: how to map data to visuals?

Set Theory

Bijection (one visual attribute, one data attribute)

Surjection (multiple visual attribute to one data attribute)

Injection (One to one mapping, but not all data elements are mapped)

Data Vars > Visual Vars ?

What happens when

Visual Vars > Data Vars ?

What happens when

Data Attributes

nominal

Non-ordered and non-numeric

AKA categorical data

[‘apple’, ‘pear’, ‘banana’]

ordinal

Ordered, not necessarily numeric

[1st, 3rd, 5th, 7th]

[‘G’, ‘PG’, ‘PG-13’, ‘R’] PG -> R

1st -> 3rd

ordinal

Ordered, not necessarily numeric

[1st, 3rd, 5th, 7th]

[‘G’, ‘PG’, ‘PG-13’, ‘R’] PG -> R

1st -> 3rd

length is not meaningful

interval

Ordered, numeric, not ratio-able

[‘Jan 12’, ‘Jan 20’]

[’17°’, ‘44°’, ‘23°’, ‘30°’]

Jan 12/Jan 20 = ???

23° / 30° = ???

ratio

Ordered, numeric, ratio-able (has a “true” 0)

[1, 3, 5, 7]

[ 5’8”, 6’1”, 5’4” ]

Ratio / Interval (Q)

Ordinal

Nominal

Q -> O[0-100] —> [A, B, C, D, F]

transforms

Ratio / Interval (Q)

Ordinal

Nominal

Q -> O[0-100] —> [A, B, C, D, F]

O -> N[A, B, C, D, F] -> [B, C, F, D, A]

transforms

Ratio / Interval (Q)

Ordinal

Nominal

Q -> O[0-100] —> [A, B, C, D, F]

O -> N[A, B, C, D, F] -> [B, C, F, D, A]

N -> O[“Jack”, “Alex” ] -> [“Alex”, “Jack”]

transforms

transforms

Ratio / Interval (Q)

Ordinal

Nominal

Q -> O[0-100] —> [A, B, C, D, F]

O -> N[A, B, C, D, F] -> [B, C, F, D, A]

N -> O[“Jack”, “Alex” ] -> [“Alex”, “Jack”]

O -> Q“Alex”+”Jack” -> 7 ???

Interval

OrdinalNominal

operations

== != > < <= >=

+ -Ratio

/ *

Interval

OrdinalNominal

operations

== != > < <= >=

+ -Ratio

/ *

consider a distance function…

Attribute TypesCategorical

OrderedOrdinal

Quantitative

structure

Tables

Attributes (columns)

Items (rows)

Cell containing value

Networks

Link

Node (item)

Trees

Fields (Continuous)

Attributes (columns)

Value in cell

Cell

Multidimensional Table

Value in cell

Grid of positions

Geometry (Spatial)

Position

Dataset Types

Tables

Attributes (columns)

Items (rows)

Cell containing value

Networks

Link

Node (item)

Trees

Fields (Continuous)

Attributes (columns)

Value in cell

Cell

Multidimensional Table

Value in cell

Grid of positions

Geometry (Spatial)

Position

Dataset Types

Tables

Attributes (columns)

Items (rows)

Cell containing value

Networks

Link

Node (item)

Trees

Fields (Continuous)

Attributes (columns)

Value in cell

Cell

Multidimensional Table

Value in cell

Grid of positions

Geometry (Spatial)

Position

Dataset Types

time

record #

Tables

Attributes (columns)

Items (rows)

Cell containing value

Networks

Link

Node (item)

Trees

Fields (Continuous)

Attributes (columns)

Value in cell

Cell

Multidimensional Table

Value in cell

Grid of positions

Geometry (Spatial)

Position

Dataset Types

sample

record #

Why?

How?

What?

Datasets

What?Attributes

Dataset Types

Data Types

Data and Dataset Types

Dataset Availability

Static Dynamic

Tables

Attributes (columns)

Items (rows)

Cell containing value

Networks

Link

Node (item)

Trees

Fields (Continuous)

Geometry (Spatial)

Attributes (columns)

Value in cell

Cell

Multidimensional Table

Value in cell

Items Attributes Links Positions Grids

Attribute Types

Ordering Direction

Categorical

OrderedOrdinal

Quantitative

Sequential

Diverging

Cyclic

Tables Networks & Trees

Fields Geometry Clusters, Sets, Lists

Items

Attributes

Items (nodes)

Links

Attributes

Grids

Positions

Attributes

Items

Positions

Items

Grid of positions

Position

Tables

Attributes (columns)

Items (rows)

Cell containing value

Networks

Link

Node (item)

Trees

Fields (Continuous)

Attributes (columns)

Value in cell

Cell

Multidimensional Table

Value in cell

Grid of positions

Geometry (Spatial)

Position

Dataset Types

data shapes the algorithm space

data shapes the visual space

Further ReadingStevens, Stanley Smith. "On the theory of scales of measurement." (1946).

Visual Attributes

Bertin, Semiologie Graphique, ‘67

(pay attention to your how you judge these differences)

Position (Common Scale)

-scatterplots -bar charts -line charts -???

Position (Un-aligned Scale)

-stacked bars -stacked area -???

Use design elements to compensate!

Angle

Volume

Accurate encoding does not ensure accurate perception!

Luminance and Saturation— really the same?

Identity

Spatial Region

Hue bad for magnitude:

Hue is great for identity:

Hue bad for magnitude:

Shape

Demiralp et al., 2014

Shape

Demiralp et al., 2014

Motion

(huge attention grabber, use with caution)

Data Vars > Visual Vars ?

What happens when

Visual Vars > Data Vars ?

What happens when

Lab: Data Deconstruction

A2:Visualization, 10 ways