1. BEYOND EXCEL Blake Umberham, OMS-II Matt Vassar, PhD

2. Learning Objectives Introduce common problems with graphical presentation of data. Introduce other applications to graphs in research. Introduce several different graphical editing and creation software in order to branch out from the monotony of excel.

5. Three dimensional representation of this data does not allow the reader in this instance to evaluate the data.

6. Using a separate bar for each individual is unnecessary and difficult to read. Using dots for each individual could have been considered.

7. Although strikingly simple, this graph does not add much to the paper as a whole.

9. Why Data Visualization? Telling stories with data! A picture is worth a thousand words Dramatically demonstrates ideas

10. What is the question? Is it well posed? What does the data say? Is it relevant to the questions? Do the visuals present the data in a clear, concise manner that answers the questions?

11. Visual Perception and Design Color vs. Shape Choosing color schemes: paletton.com colorbrewer2.org Allows to view on grayscale version of your color scheme. Color Blindness: vischeck.com Allows you to view your visualization scheme through the lens of color blind individual.

12. paletton.com

13. Colorbrewer2.org

14. vischeck.com

15. Texture If your journal does not allow you to use color, consider using texture to delineate between sets of data.

16. How are graphs used in research? Historically seen in the results section to display data; however, lately we are seeing diagrams, graphs, and other visualization aides used in the methods. Why? Visually appealing Efficiency in getting information to the reader Clearer depiction of methodological aspects of the research Creates transparency in data collection and reporting

17. CONSORT 2010 Flow Diagram: Randomized Controlled trials.

19. PRISMA Diagram: Systematic Reviews

21. PlosOne Article Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm Looked at 703 articles from top physiology journals. Found that for SMALL SAMPLE size studies are misusing bar and line graphs. Bar graphs are designed for categorical variables; yet they are commonly used to present continuous data in laboratory research, animal studies, and human studies with small sample size. Encourage different practices to display data such as Scatter-plots, Box plots, and histograms. Scatter plots prompt the reader to critically evaluate the authors analysis and interpretation of the data, the bar graphs discourage the reader from thinking about these issues by masking distributional information.

22. Many different data distributions lead to the same bar or line graph.

23. A. The bar graph suggests that the groups are independent and provides no information About whether changes are consistent across individuals. B – D. Clearly demonstrates that the data are paired.

24. Data Visualization Tools Try it for yourself! Become comfortable with visualization tools. Keep in mind: the best way to represent your data the exact way you want to represent it is by coding.

25. Data Visualization Techniques Point and Click:To Code or Not To Code: Easy to learn Provide quality graphical representation of data Time consuming to reproduce graphs Proprietary Often difficult to learn Provides customized visualization of data. Easy to reproduce or modify Open and free

26. AMCHARTS

27. Plot.ly

28. Online collaboration with research partners. Simply email them the URL or share the graph with them via the plot.ly website. Quality graphs that are poster/paper/presentation ready! Explore charts being shared on the home page to gain inspiration for you own creation of data representation.

29. Plot.ly: OKStars Project

30. HighCharts

31. Datacopia

33. Summary of Point and Click Visualization Techniques AMCHARTS Plot.ly Highcharts Datacopia Tableau Google Developers Charts Qlik There is a multitude of options out there! Choose one and try it out.

34. To Code or NOT To Code R: sp, rgdal rgeos igraph network ggvis shiny Python: pySAL arcpy networkkx graphtool

35. Additional Resources NIH website The Functional Art: An Introduction to Information Graphics and Visualization. By Alberto Cairo The Wall Street Journal Guide to Information Graphics: The Dos and Don’ts of Presenting Data, Facts, and Figures. By Dona Wong

36. Acknowledgements Dr. Matt Vassar Brandy Close

37. Thank you!

38. References http://syntheticdaisies.blogspot.com/p/graphical-free- association.html http://www.biostat.wisc.edu/%7Ekbroman/topten_worstgrap hs/ NIH Library