Grammar  of  Graphics:  

Visualizing  Data Leslie McIntosh

Machine Learning & Data Science Meetup February 2013 – St. Louis, MO

Overview •  Problem •  Grammar of Graphics – Leland Wilkenson

o  Syntax o  Semantics

•  Ggplot – Hadley Wickham •  Example - Leslie •  Example - Divya •  Q&A

Background  &  Perspective

How  do  you  obtain  data?

How  do  you  get  meaningful  information  

from  data?

Where  is  visualization  in  the  data  process  cycle?

What  is  a  statistical  graphic?

Leland Wilkinson: The Grammar of Graphics 2nd Edition

Premises •  Pipeline does not mandate implementation of

system

•  Cannot change the order of the pipeline

Syntax

Step  1:    Extract  data  from  source  

to  create  variables

Variables

Step  2:    Apply  algebra  to  varset

Algebra

Step  3:    Apply  scales  to  varset

Scales

Step  4:    Compute  statistics    

on  varset

Statistics

Step  5:    Construct  geometry  to  

produce  graph

Geometry

Step  6:    Apply  coordinates  to  graph

Coordinates

Step  7:    Compute  aesthetics  to  

create  graphic

Aesthetics

Aesthetic  AQributes Form Surface Motion Sound Text Position Size Shape   •  Polygon •  Glyph •  Image Rotation Resolution  

Color •  Hue •  Brightness •  Saturation Texture •  PaQern •  Granularity •  Orientation Blur Transparency

Direction Speed Acceleration

Tone Volume Rhythm Voice

Label

Semantics

Space •  Two spaces to make a graphic

o  The underlying space o  Display space

Time •  Time is ordered •  Time is continuous •  Time zones are discrete •  Time intervals are additive •  Time is cyclical •  Time is independent of location (perceived)

Uncertainty •  Variability •  Noise •  Incompleteness •  Indeterminacy •  Bias •  Error •  Accuracy •  Precision •  Reliability •  Validity •  Quality •  Integrity

Graphic: Hadley Wickham docs.ggplot2.org

Analysis

!

Graphic: Leslie McIntosh

Control  &  Automation •  Divya’s example

Tools  of  Data  Visualization

•  R o  ggplot2 - Hadley Wickham o  Shiny

•  SPSS o  Leland Wilkinson et al

•  SAS •  D3 •  GGobi

ggplot2  Properties •  Data •  Scales •  Stats •  Geoms •  Coordinates •  Faceting •  Themes

Scaling •  Controls the mapping between data and

aesthetics •  Can be mapped to a variable or be constant •  Examples

o  Legend o  Colour-gradient o  Shape

Geoms •  Determine the type of plot •  Examples

o  Area o  Bars o  Boxplot o  Histogram o  Raster o  Ribbon

Aesthetics •  Can be mapped to a variable or be constant

Examples

Grammar  of  Graphics  and  ggplot  describe  rules  and  give  a  tool,  

but  not  how  to  decide  to  properly  represent  data  

 Perinatal/Infant  Mortality  Rates  of  Delayed-­‐‑Delivered  Second  Twin    

0%

20%

40%

60%

80%

Gestational Age (Weeks)

Delay 61% 37% 18% 7%

No Delay 74% 37% 9% 4%

18-23 24-26 27-29 30+

It  is  easy  to  deceive  with  poor  visualizations,  it  is  more  difficult  to  portray  the  truth  without  them.  

Incremental  changes  in  aesthetics  should  reflect  and  be  perceived  as  proportional  and/or  

meaningful  changes  in  data

Example

Communication  Network

Insurance  Map

Final  Comments