How to

think about data

Shirley Wu

@sxywu

shirley wu

 

independent creative

focused on data-driven

art & visualizations

(sxywu.com)

custom data visualizations can be categorized into two broad categories:

EXPOSITORY

VS.

EXPLORATORY

expository

- static dataset

- explore data for story

- communicate story to audience

exploratory

- dynamic dataset

- interview stakeholders

- build tool for stakeholders to explore the data 

examples:

New York Times, The Pudding, The Washington Post, etc.

examples:

scientific visualizations, internal business tools at Netflix, Uber, Airbnb, etc.

expository

Data: top 10 blockbusters every year for the last two decades

 

Goal: come up with a design and implement it together

(Yay participation!)

 

Raw data in JSON / CSV

Data exploration

with Observables

and Vega-Lite

Design with

Marks, Channels

and Gestalt Laws

Code with

SVG paths

and D3.js shapes, layouts

Finish with

annotations, axes, legends

Data exploration

with Observables

and Vega-Lite

Design with

Marks, Channels

and Gestalt Laws

Code with

SVG paths

and D3.js shapes, layouts

Finish with

annotations, axes, legends

data exploration

  1. List data attributes
  2. Ask questions
  3. Explore the data

 

We will use an Observable notebook for this.

data exploration:
data types

  • Categorical (movie genres)
  • Ordinal (t-shirt sizes)
  • Quantitative (ratings/scores)
  • Temporal (dates)
  • Spatial (cities)

data exploration

List all the attributes,

ask all the questions!

(Notebook)

(Quick Observable Guide)

data exploration:

some basic Chart types

Bar chart

For categorical comparisons

 

Domain: categorical

Range: quantitative

data exploration:

some basic Chart types

Histogram

For categorical distributions

 

Domain: quantitative bins

Range: frequency of quantitative bin

data exploration:

some basic Chart types

Scatterplot

For correlation

 

2 categories, and the relationship between their quantitative values

data exploration:

some basic Chart types

Line chart

For temporal trends

 

Domain: temporal

Range: quantitative

data exploration

Brainstorm some charts

to answer the questions.

data exploration:

exercise (together)

Starter notebook

Full notebook

Vega-Lite

data exploration:

More charting tools

data exploration:
advice

  • Check for missing data, and the validity of the data
  • Focus on one question at a time (it's very easy to get sidetracked with a tangent)
  • If there IS an interesting tangent, make a note for later
  • If the question leads to a dead-end, explore another question or the tangent you found earlier
  • Don't be afraid to go out and look for additional data to aid your exploration
  • Sometimes, no interesting pattern IS very interesting

Translate from
data to design

  1. Concentrate on the takeaways to communicate across
  2. What does that mean in terms of the data?  (Individual or aggregate elements? Which attributes?)
  3. Map the relevant data to visual elements

Design:
marks & channels

Map individual or

aggregate data

elements to marks.

 

Map data attributes

to channels.

Design:
marks

Visualization Analysis and Design. Tamara Munzner, with illustrations by Eamonn Maguire. A K Peters Visualization Series, CRC Press, 2014.

Design:
channels

Visualization Analysis and Design. Tamara Munzner, with illustrations by
Eamonn Maguire. A K Peters Visualization Series, CRC Press, 2014.

Quantitative

  • Position
  • Size
  • Color

Categorical

  • Shape
  • Texture
  • Color

Temporal

  • Animation

Design:
marks & channels

Visualization Analysis and Design. Tamara Munzner, with illustrations by
Eamonn Maguire. A K Peters Visualization Series, CRC Press, 2014.

mark

bar

channels

x: category

y: quant

mark

point

channels

x: quant

y: quant

mark

point

channels

x: quant

y: quant

color: category

mark

point

channels

x: quant

y: quant

color: category

size: quant

Design:
channel effectiveness

Visualization Analysis and Design. Tamara Munzner, with illustrations by
Eamonn Maguire. A K Peters Visualization Series, CRC Press, 2014.

Design:
marks & channels

  • One-to-one mapping of data to channel

  • Multiple mappings of channel to mark (x, y, size, color usually)

  • Do not EVER map multiple data attributes to the same channel

Design:
Gestalt laws of grouping

the human mind naturally

groups individual elements

into patterns

use in data visualization to

save processing time

 

 

Design:
Gestalt laws of grouping

Proximity

Put related objects near each other

(The Functional Art, Ch. 6 by Alberto Cairo)

Design:
Gestalt laws of grouping

Similarity

Indicate like objects (helpful if they can't be placed close to each other)

(The Functional Art, Ch. 6 by Alberto Cairo)

Design:
Gestalt laws of grouping

Enclosure

Helpful when creating visualizations with multiple sections

(The Functional Art, Ch. 6 by Alberto Cairo)

Design:
visual metaphors

Take advantage of what people are already familiar with

design:
exercise

Sketch all the things!

  1. What is your main message(s)?
  2. What marks will you use?  Do they represent individual data points, or aggregate?
  3. What channels will your marks use?  How do they support your message?

readability

Titles, descriptions, and legends

to explain the visualization

 

Axes and annotations

to describe the data

READABILITY:
axes & legends

d3-legend by Susie Lu

READABILITY:
Annotations

d3-annotation by Susie Lu

more svg for

context & aesthetics

  • Patterns
  • Gradients
  • Text on a path
  • SVG filters
    (blurs, drop-shadows)
  • Clipping & masking

Movies have the biggest box office

during the summer and winter holidays

resources

Books:

The Functional Art by Alberto Cairo

Visual Analysis and Design by Tamara Munzner

 

Online:

Datawrapper Blog

Flowing Data

The little of visualization design

The Pudding

Information is Beautiful Awards

 

My coding

workshops

Workshop: How to Tell Stories With Data

By Shirley Wu

Workshop: How to Tell Stories With Data

For NASA/JPL, 8/9/2019

  • 93
Loading comments...

More from Shirley Wu