DSC 201: Data Analysis & Visualizationdkoop/dsc201-2016fa/lectures/lecture15.pdf · MTA Fare Data Exploration D. Koop, DSC 201, Fall 2016 7 A UGUST S U N M O N T U E W E D T H U F

DSC 201: Data Analysis & Visualization

Visualization Tools

Dr. David Koop

D. Koop, DSC 201, Fall 2016

Visualization for Exploration

2D. Koop, DSC 201, Fall 2016

MTA Fare Data Exploration














A U G U S TS U N M O N T U E W E D T H U F R I S A T

2 3

10

17

24

31

9

16

23

30

SD SDHOU DETDETT OR DET

DET DETCHW COLCHWSD CHW

BOS BOSLAA LAALAADET LAA

TB TBTOR TORTORBOS TOR

BAL BALTOR TORTORTB TOR

1

8

15

22

29

1

7

14

21

28

3

6

13

20

27

2

5

12

19

26

1

4

11

18

25

1:10 1:10 10:10 8:40

8:104:10 8:10 8:10 1:10 7:05 1:05

7:05TBA 7:05

7:071:40 7:07 7:077:07 7:05 1:05

7:05 1:05 7:10 4:05

1:10TBA 7:05 7:05 1:05 7:10 7:10

YES YES YES

YES YES MY9 YES YES YES YES

TBA YES YES YES YES MY9 FOX

TBA YES MY9 YES YES MY9 YES

YES YES YES YES YES YES YES

S E P T E M B E RS U N M O N T U E W E D T H U F R I S A T

6 7

14

21

28

30

13

20

27

29

BOS BOSCHW BOSCHWBAL CHW

BOS BOSBAL BALBALBOS BAL

SF SFTORTOR TOR TORBOS

HOU HOUTB TBTBSF TB

T OR T ORCHW CHWHOUHOU HOU

5

12

19

26

28

4

11

18

25

27

3

10

17

24

30

2

9

16

23

30

1

8

15

22

29ALL GAMES ARE EASTERN TIME.

1:051:05 7:05 7:05 7:05 7:05 1:05

7:05TBA 7:05 7:05 7:05 7:10 1:05

1:10TBA 7:07

1:102:10 1:10

7:07 7:07 7:05 TBA

1:101:05 7:05 7:05 7:05 8:10 TBA

TBA YES MY9 YES YES MY9 FOX

YES YES YES YES YES YES FOX

TBA YES MY9 YES YES YES TBA

YES YES

YES YES

MY9 YES YES YES TBA

2 013 R E G U L A R S E A S O N S C H E D U L E

Visualization for Explanation


Design Iteration


http://kpq.github.io/chartsnthings/2013/09/19-sketches-of-quarterback-timelines.html

http://kpq.github.io/chartsnthings/2013/09/19-sketches-of-quarterback-timelines.html

Definition

“Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively.”

— T. Munzner


Definition



Definition



NYC Subway Fare Data

Definition



Find Interesting NYC Subway Ridership Patterns

NYC Subway Fare Data

Definition



Computers Helping People


[Cerebral, Barsky et al., 2007]

http://www.cs.ubc.ca/labs/imager/tr/2007/barskya_cerebral_appnote/

Definition

“Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively”


●

●●

●●

●

●

●

●

●●

4 6 8 10 12 14 16 18

4

6

8

10

12

x1

y 1

●●

●●●

●

●

●

●

●

●

4 6 8 10 12 14 16 18

4

6

8

10

12

x2

y 2●

●

●

●●

●

●●

●

●●

4 6 8 10 12 14 16 18

4

6

8

10

12

x3

y 3

●●

●

●●

●

●

●

●

●

●

4 6 8 10 12 14 16 18

4

6

8

10

12

x4

y 4

Why Visual?


[F. J. Anscombe]

Definition



Design Iteration


http://chartsnthings.tumblr.com/post/62679766588/19-sketches-of-quarterback-timelines

http://chartsnthings.tumblr.com/post/62679766588/19-sketches-of-quarterback-timelines

Definition



Effectiveness


[S. Hayward, 2015]

http://www.powerlineblog.com/archives/2015/10/the-only-global-warming-chart-you-need-from-now-on.php

Visualization Tools• Analysis Apps: Tableau, Excel, SAS • Illustration Apps: Illustrator, Inkscape • R Libraries: base, ggplot • Python Modules: matplotlib, seaborn, bokeh, altair • Lower-level Frameworks: D3, Processing • Many, many more: Google "data visualization tools"


This Class• Tableau • Python modules: matplotlib, bokeh


Data Types• Items

- An item is an individual discrete entity - e.g. row in a table, node in a network

• Attributes - An attribute is some specific property that can be measured,

observed, or logged - a.k.a. variable, (data) dimension, column in a table


22

Fieldattribute

item

Items & Attributes


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

[Munzner (ill. Maguire), 2014]

Attribute Types


Attribute Types

Ordering Direction

Categorical Ordered

Ordinal Quantitative

Sequential Diverging Cyclic

[Munzner (ill. Maguire), 2014]

231 = Quantitative2 = Nominal3 = Ordinal

quantitative ordinal categorical

Categorial, Ordinal, and Quantitative


241 = Quantitative2 = Nominal3 = Ordinal

quantitative ordinal categorical

Categorial, Ordinal, and Quantitative


Tableau Overview• Grew out of research at Stanford University on how to explore

multidimensional datasets & relational databases • Tableau Desktop: standalone (free trial, student license) • Tableau Public: cloud-based system (free) • Tableau Vizable: mobile app • Tableau's Introduction Videos • https://www.youtube.com/watch?v=6py0jyZc7K4


https://www.youtube.com/watch?v=6py0jyZc7K4

Tableau• High-level GUI that connects to data, helps organize it, and

provides intuitive routines for visualizing it plus customization • Lots of possibilities • Great for exploration


Tableau Example


Data In Tableau• Categorical data = Dimension • Quantitative data = Measures


matplotlib• The workhorse of python visualization • seaborn builds on top of matplotlib • Many new kids on the block: bokeh, altair


matplotlib• %matplotlib inline (show plots in the notebook!) • Always create a figure first, then draw plots • Lots of high-level plotting types (line plots, scatterplots, histograms) • Lots of customizability


Shortcuts in pandas• Connect directly to pandas data frames

- df.plot • http://pandas.pydata.org/pandas-docs/stable/generated/

pandas.DataFrame.plot.html


http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html

Documents

DSC 201: Data Analysis & Visualizationdkoop/dsc201-2016fa/lectures/lecture15.pdf · MTA Fare Data Exploration D. Koop, DSC 201, Fall 2016 7 A UGUST S U N M O N T U E W E D T H U F