Effective graphics

STAT1003 – Statistical Techniques

Dr. Emi Tanaka

Australian National University

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Table of contents

• Why data visualisation?
• Data Visualisation Catalogue 🛒 What plot type to use?
• Why is a 3D pie chart considered a “bad plot”?
• What about 2D pie charts?
• Elementary perceptual tasks
• Retrieving information from graphs
• Preattentive processing
• Gestalt principles
• Case study Birth place from the 2021 Australian Census
  • Plot #1
  • Plot #2
  • Plot #3
  • Plot #4
  • Another data story
  • Plot #5
  • Plot #6
  • Plot #7
  • Plot #8
  • Plot #9
• Color palettes
  • Qualitative palettes
  • Sequential palettes
  • Diverging palettes
  • Colorblindness
  • Using different color palettes in ggplot2
  • Summary

Why data visualisation?

• “A picture is worth a thousand words”
• Data visualisation can make large, complex data more accessible, understandable and usable.

Data visualization is part art and part science. The challenge is to get the art right without getting the science wrong and vice versa.

– Claus O. Wilke, Fundamentals of Data Visualization

• Effective data visualisation means to design your data plot to effectively use human visual system to improve cognition about a targeted information from the data.

Note: we interchangeably use the terms data visualisation, plot, data plot, graphic, statistical graphic, and figure.

Data Visualisation Catalogue 🛒 What plot type to use?

Non-exhaustive

Why is a 3D pie chart considered a “bad plot”?

What about 2D pie charts?

Which category has the largest percentage?

help("pie")

Pie charts are a very bad way of displaying information. The eye is good at judging linear measures and bad at judging relative areas. A bar chart or dot chart is a preferable way of displaying this type of data.

• This comes from empirical research of Cleveland & McGill (1984) among others.

Cleveland, William S., and Robert Mc Gill. (1984) “Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods.”

Elementary perceptual tasks

Non-exhaustive

Retrieving information from graphs

Of the 10 elementary perception tasks, Cleveland & McGill (1984) found the accuracy ranked as follows…

Rank 1

Example

Rank 2

Example

Rank 3

Example

Rank 4

Example

Rank 5

Example

Rank 6

Example

Preattentive processing

Viewers can notice certain features are absent or present without focussing their attention on particular regions.

• Which plot helps you to distinguish the data points?

Healey, Christopher G., and James T. Enns (2012) “Attention and Visual Memory in Visualization and Computer Graphics.” IEEE Transactions on Visualization and Computer Graphics 18 (7): 1170–88.

Gestalt principles

• “Gestalt” is German for form or shape.
• Gestalt principles are a set of laws to address the natural compulsion to find order in disorder by perceiving a series of individual elements as a whole.

• Law of proximity: Elements that are close together are perceived as being related.
• Law of similarity: Elements that share similar characteristics are perceived as belonging together.
• Law of closure: Elements that are arranged in a way that suggests a complete form are perceived as a whole.

Case study Birth place from the 2021 Australian Census

Birth place	Count	%
Australia	17,020,422	66.9
Not Stated	1,358,658	5.3
England	927,490	3.6
Other	759,173	3.0
India	673,352	2.6
China	549,618	2.2
New Zealand	530,492	2.1
Philippines	293,892	1.2
Vietnam	257,997	1.0
South Africa	189,207	0.7
Malaysia	165,616	0.7
Italy	163,326	0.6
Sri Lanka	131,904	0.5
Nepal	122,506	0.5
Scotland	118,496	0.5
Korea South	102,092	0.4
United States America	101,309	0.4
Germany	101,255	0.4
Hong Kong	100,148	0.4
Iraq	92,922	0.4
Greece	92,314	0.4
Pakistan	89,633	0.4
Lebanon	87,340	0.3
Indonesia	87,075	0.3
Thailand	83,779	0.3
Ireland	80,927	0.3
Iran	70,899	0.3
Fiji	68,947	0.3
Netherlands	66,481	0.3
Singapore	61,056	0.2
Afghanistan	59,797	0.2
Bangladesh	51,491	0.2
Canada	50,223	0.2
Taiwan	49,511	0.2
Brazil	46,720	0.2
Poland	45,884	0.2
Japan	45,267	0.2
Croatia	43,302	0.2
Egypt	43,213	0.2
North Macedonia	41,786	0.2
Zimbabwe	39,714	0.2
Myanmar	39,171	0.2
Cambodia	39,043	0.2
Turkey	38,568	0.2
France	36,019	0.1
Malta	35,413	0.1
Papua New Guinea	29,984	0.1
Chile	29,860	0.1
Wales	29,250	0.1
Samoa	28,107	0.1
Bosnia Herzegov	26,171	0.1
Mauritius	25,981	0.1

Plot #1

Which birth place is the third largest among people in Australia?

Plot #2

Can you read the labels without tilting your head?

Plot #3

What’s the data story?

Plot #4

• The text on the bar shows the percentage out of 25,422,788 Australian residents born in the corresponding country.

• There were 5.3% of Australian residents who did not state their birth place.

• The top country of birth place is Australia with 66.9% of Australian residents born in Australia.

Story from The Guardian.

Another data story

Data Story

India has overtaken China and New Zealand to become the third largest country of birth for Australian residents, 2021 census data has found.

– The Guardian

Birth place	Count	%	Census Year
England	907,570	3.9	2016
New Zealand	518,466	2.2	2016
China	509,555	2.2	2016
India	455,389	1.9	2016
Philippines	232,386	1.0	2016
England	927,490	3.6	2021
India	673,352	2.6	2021
China	549,618	2.2	2021
New Zealand	530,492	2.1	2021
Philippines	293,892	1.2	2021

Plot #5

Does this show that India overtook China and New Zealand?

Plot #6

Should we show percentage instead of counts?

Plot #7

The legend and the line order is different…

Plot #8

Maybe we can put the labels directly in the plot?

Plot #9

Color palettes

Qualitative palettes

• Designed for nominal variable (no particular ordering)

Zeileis, Fisher, Hornik, Ihaka, McWhite, Murrell, Stauffer, Wilke (2019). colorspace: A Toolbox for Manipulating and Assessing Colors and Palettes. arXiv 1903.06490

Zeileis, Hornik, Murrell (2009). Escaping RGBland: Selecting Colors for Statistical Graphics. Computational Statistics & Data Analysis 53(9) 3259-3270

Sequential palettes

• Designed for ordered categorical variable or number going from low to high (or vice-versa)

Diverging palettes

• Designed for ordered categorical variable or number going from low to high (or vice-versa) with a neutral value in between

Colorblindness

Colorblindness affect roughly 1 in 8 men.

Using different color palettes in ggplot2

• To change the color scale, you can use functions starting from
  • scale_color_ (for points, lines, etc.) or
  • scale_fill_ (for bars, areas, etc.) in ggplot2.

Summary

• Data visualisation can make large, complex data more accessible, understandable and usable.
• An effective data visualisation means to design your data plot to effectively use human visual system to improve cognition about a targeted information from the data.
• Empirical research has found that the accuracy of retrieving information from graphs is ranked as follows: position on common scale > position on non-aligned scale > length > direction > angle > area > volume > curvature > color shade/saturation > color hue.
• Preattentive processing allows viewers to notice certain features are absent or present without focussing their attention on particular regions.
• Gestalt principles are a set of laws to address the natural compulsion to find order in disorder by perceiving a series of individual elements as a whole.
• Choosing the right plot type and design can help to effectively communicate the data story.
• There are three types of color palettes: qualitative (for nominal variable), sequential (for ordered variable), and diverging (for ordered variable with a neutral value in between).
• When choosing colors for data visualisation, it is important to consider colorblindness.