Data Visualization

Outline

• Basic points
• Graphs
  • Excel
  • Matlab
  • R/Mathematica/Python
  • Other third party data
  • Tableau?
• Tables
• CAE/CFD
• 4D Data
• Other
  • Circle things on pictures

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

• most common form of data visualization
• Pls no grid lines if possible reference: https://www.perceptualedge.com/articles/dmreview/grid_lines.pdf
  • Tick marks
  • Colors
  • Leader lines
• Pls no legend
  • Direct Labels
  • Leader lines
• Lines not pencil thin
• Scatter plots most common
  • Considered bad form to use lines to connect discrete data points
  • Use trendline sparingly, only when r2 or slope is point of the graph

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