Effective Strategies for Creating Scientific graphics

Effective strategies for scientific graphics

Joel Kellyjkelly@chem.ubc.ca

October 31, 2013

Thursday, October 31, 2013

2

Why care about graphics as a scientist?

Thursday, October 31, 2013

Quick Poll

3

Thursday, October 31, 2013

4

Data

Graphics (scatterplot,spectrum, micrograph, etc)

Presentation(to your professor, other

researchers, general public)

Exploration:“What conclusions

do my data support?”

Visual Intuition

Thursday, October 31, 2013

Graphics reveal data.

• Dual purposes: to explore data, and to present data.

• Excellent graphics do so with clarity, efficiency and precision.

• Richness beyond what any summary statistics (average, standard deviation, correlation, etc) can provide.

5

Thursday, October 31, 2013

Anscombe’s quartet

• All datasets: mean, variance & correlation are all identical

6

Thursday, October 31, 2013

Scientific graphics should:

• Show the data

• Allow the viewer to think about the substance, rather than the methodology of the experiment (or something else- font/color/etc)

• Avoid distorting the data

• Reveal multiple layers of detail: big picture & fine structure

7

Thursday, October 31, 2013

Some examples: the bad

8

Thursday, October 31, 2013

Some examples: the bad

9

Lie factor: effect shown in graphic effect shown in data

Thursday, October 31, 2013

• Bad data = bad graphics!

10

Some examples: the bad

Thursday, October 31, 2013

Types of graphics

• Most popular in mass media:time series & data maps

• Excel is optimized for business users (earnings reports, market share, etc).

• Beware “chartjunk”!

• Chemistry is most concerted with relational graphics.

11

Thursday, October 31, 2013

Effective strategies:1. Maximize “data ink”: % of graphic

actually used to plot your data.

2. Maximize data density.

• Use small multiples

• Combine graphics, images & numbers to tell a visual story.

3. Use color effectively

4. Revise & edit.

12Tufte’s rules: http://www.sealthreinhold.com/tuftes-rules/index.phpThursday, October 31, 2013

Maximize data ink:

13

Thursday, October 31, 2013

Maximize data ink:

14

Thursday, October 31, 2013

Data density

15dougmccune.com/blog

Small multiples:

Thursday, October 31, 2013

Data density

16

Frankel & DePace. “Visual Strategies: A Practical Guide to Graphics for Scientists and Engineers” (Yale University Press 2012)

Thursday, October 31, 2013

Data density

• Not all data needs to be presented as a graphic

• For example, tables are sometimes more effective for small data sets

17(most infographics are silly)

Thursday, October 31, 2013

18

(some infographicsare pretty neat!)

from Wired Magazine’s best infographics & scientific figures

Thursday, October 31, 2013

Using color effectively

• Colorbrewer (colorbrewer2.org)

• Colorblind people are scientists too!

• Beware the black & white photocopier19

Sequential: data that runs from low to highDiverging: emphasize max/min extremes of dataQualitative: no difference implied between data classes (best for nominal/categorical data)

Thursday, October 31, 2013

Using color effectively

• Minimal color highlights data: builds a visual story

20

Thursday, October 31, 2013

Tools of the trade

• Understand the limitations of Excel

• Lots of field-specific options: gnuplot, Origin, R, Matlab, SPSS, Sigmaplot, etc. (see handout)

• Revise and edit: develop your own personal style

21

Thursday, October 31, 2013