Effective Strategies for Creating Scientific graphics
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Effective Strategies for Creating Scientific graphics

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Effective Strategies for Creating Scientific graphics Effective Strategies for Creating Scientific graphics Presentation Transcript

  • Effective strategies for scientific graphics Joel Kelly jkelly@chem.ubc.ca October 31, 2013 Thursday, October 31, 2013
  • Why care about graphics as a scientist? 2 Thursday, October 31, 2013
  • Quick Poll 3 Thursday, October 31, 2013 View slide
  • 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 View slide
  • 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 Lie factor: effect shown in graphic effect shown in data 9 Thursday, October 31, 2013
  • Some examples: the bad • Bad data = bad graphics! 10 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. Tufte’s rules: http://www.sealthreinhold.com/tuftes-rules/index.php Thursday, October 31, 2013 12
  • Maximize data ink: 13 Thursday, October 31, 2013
  • Maximize data ink: 14 Thursday, October 31, 2013
  • Data density Small multiples: dougmccune.com/blog Thursday, October 31, 2013 15
  • Data density Frankel & DePace. “Visual Strategies: A Practical Guide to Graphics for Scientists and Engineers” (Yale University Press 2012) Thursday, October 31, 2013 16
  • 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
  • (some infographics are pretty neat!) from Wired Magazine’s best infographics & scientific figures 18 Thursday, October 31, 2013
  • 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
  • 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