Data Visualization

Basic Points

• Show what is hard to tell
• Easy to read
• Obvious point
• Print vs Digital
  • Print is smaller, colors come out worse, lines not as crisp
  • If in journal... expect no colors, extremely small

Graphs

Graphs are the most common form of data visualization. Despite their ubiquity, often graphs fall short of their ability to convey information. The way the graphs are constructed can be as important as the data they are displaying. Graphs can fail in a number of ways: being cluttered, being vague, being complex, being imperceptible, etc.

Cluttering a graph is often a symptom of not putting enough work into the visualization rather than too much. Default graphing programs will often add superfluous elements. One of the most common and egregious elements in this vein are grid lines. Grid lines should never be added to a chart by default, only if needed, and when added, often with more moderation than a default program will do so. Tick marks and color differentiation should be more than enough in most cases to elucidate the point. See here for further reading:

• https://www.perceptualedge.com/articles/dmreview/grid_lines.pdf

Another chart element that often ends up being confusing is a legend. It takes up room that the actual data could occupy and forces the reader to stop looking at the information itself while trying to understand it, which dramatically impedes comprehension. Instead, it is recommended to use direct labels on the data. There are times and places that a legend is necessary, but similar to gridlines, it should be a when it becomes helpful, not because it is easy or already generated.
See here for further information:

• https://stephanieevergreen.com/directly-label/
• https://www.datarevelations.com/avoid-color-legends/
• https://depictdatastudio.com/accessibility-quick-wins-remove-legends-and-directly-label/

Trend lines are less frequently abused than grid lines or legends, but can clutter the graph just as significantly. Trend lines are used when the graph is being used to convey a pattern or trend, or when it being used to forecast.
Further reading:

• https://www.storytellingwithdata.com/blog/2020/10/20/thoughts-on-trendlines

Additional accessibility gains can be found with characteristics of the plot most notably color and size.

Color

Color is the best way to differentiate between data sets. Not only are we hardwired to pick out color differences before any other differentiator, but we also have built-in associations with colors themselves that allows for us to tell a more complete story in less time and with less effort (eg red is hot, blue is cold).

There has been more written about color by people way smarter than me than I could hope to summarize here, so I will just lay out some of the best resources:

• http://www.kennethmoreland.com/color-maps/ColorMapsExpanded.pdf
• https://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html

Other

• shape
• big arrow or circled

Excel

• Easiest to input raw data
• Extremely easy to make it not look like dookie
• Excel likes to make text grey, fine on computer, bad in print
• Default blue is hard to read in print, also kinda ugly color
• Not good at showing 3d data, forget 4d
  • Can be done with some creativity or plug ins
• Google Sheets not as powerful, can look fancier

First steps

• Get rid of grid lines
• Set axes properly
• (Recommended) Change color from default blue to black

Matlab

Matlab is hugely useful for processing the data (although importing it can be tricky depending on the quantity and type of data you have collected, and your patience in optimizing your code) so it makes sense to plot the data in the same program. The native Matlab graphing functions are easily tuned to provide a clean, readable, and easily understood graph. Matlab is very useful for visualizing 3D data in many ways.

• Requires some programming experience
• Slightly harder to import data than excel
• The standard plotting in Matlab is very familiar to most judges

R

R is a free program used for statistical analysis and can be a great tool. It has a very steep learning curve and is likely more time than it is worth for extracting, processing, and visualizing the kinds of data most teams will be working with. The plotting functions can be extremely powerful and customizable but require the relatively large upfront investment of learning the language. If someone on the team already knows R, there's a good chance they'll know how to make a plot readable.

Tables

Tables are ubiquitous when dealing with data. Often they are dense and very difficult to parse unless the reader is familiar with the format or contents. When presenting data in tables, restrict the quantity of information and highlight the key take-away or data point. Often other forms of data visualization can be more compelling, and easier to understand.

CAE and CFD

CAE and CFD are great ways to show information about a design. It is important to include what the pretty colors mean. Because they are so compelling, simulation results can easily lead a team to commit to a decision that is not in their best interest.

Other

Qualitative data can be difficult to show on a graph. Often data that drives things like packaging, form factors, and routing are visual. These can be shown with an image of the