Effective Strategies for Creating Scientific graphics

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Effective Strategies for Creating Scientific graphics

  1. 1. Effective strategies for scientific graphics Joel Kelly jkelly@chem.ubc.ca October 31, 2013 Thursday, October 31, 2013
  2. 2. Why care about graphics as a scientist? 2 Thursday, October 31, 2013
  3. 3. Quick Poll 3 Thursday, October 31, 2013
  4. 4. Data Graphics (scatterplot, spectrum, micrograph, etc) Presentation (to your professor, other researchers, general public) Exploration: “What conclusions do my data support?” Visual Intuition 4 Thursday, October 31, 2013
  5. 5. 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
  6. 6. Anscombe’s quartet • All datasets: mean, variance & correlation are all identical 6 Thursday, October 31, 2013
  7. 7. 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
  8. 8. Some examples: the bad 8 Thursday, October 31, 2013
  9. 9. Some examples: the bad Lie factor: effect shown in graphic effect shown in data 9 Thursday, October 31, 2013
  10. 10. Some examples: the bad • Bad data = bad graphics! 10 Thursday, October 31, 2013
  11. 11. 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
  12. 12. 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. Tufte’s rules: http://www.sealthreinhold.com/tuftes-rules/index.php Thursday, October 31, 2013 12
  13. 13. Maximize data ink: 13 Thursday, October 31, 2013
  14. 14. Maximize data ink: 14 Thursday, October 31, 2013
  15. 15. Data density Small multiples: dougmccune.com/blog Thursday, October 31, 2013 15
  16. 16. Data density Frankel & DePace. “Visual Strategies: A Practical Guide to Graphics for Scientists and Engineers” (Yale University Press 2012) Thursday, October 31, 2013 16
  17. 17. Data density • Not all data needs to be presented as a graphic • For example, tables are sometimes more effective for small data sets (most infographics are silly) Thursday, October 31, 2013 17
  18. 18. (some infographics are pretty neat!) from Wired Magazine’s best infographics & scientific figures 18 Thursday, October 31, 2013
  19. 19. Using color effectively Sequential: data that runs from low to high Diverging: emphasize max/min extremes of data Qualitative: no difference implied between data classes (best for nominal/categorical data) • Colorbrewer (colorbrewer2.org) • Colorblind people are scientists too! • Beware the black & white photocopier Thursday, October 31, 2013 19
  20. 20. Using color effectively • Minimal color highlights data: builds a visual story 20 Thursday, October 31, 2013
  21. 21. 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

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