When selecting the right type of visualization for your data, think about your variables (string/categorical and numeric), the volume of data, and the question you are attempting to answer through the visualization. Additionally, think about who will be viewing the data and how you can best optimize the data narrative through design.
Cleveland and McGill (1985) studied the visual characteristics of data visualization that are the easiest and most difficult for the human eye to perceive. They are, in order of least difficult to most difficult:
1. position along a common scale
2. position along a non-aligned scale
3. length
4. angle andslope
5. area
6. volume, density, andcolor saturation
7. color hue
This means that a visualization consisting of differently sized and colored bubbles is more difficult for the human eye to discern than a bar chart (position along a common scale).
Cleveland, William S., and Robert McGill. 1985, "Graphical perception and graphical methods for analyzing scientific data." Science299 (4716):828-833.
For in depth information on all of the figures discussed below, please see:
Zoss, Angela M. "Designing Public Visualizations of Library Data." In Data Visualization: A Guide to Visual Storytelling for Librarians, edited by Lauren Magnuson,. Lanham, MD: Rowman & Littlefield Publishers, Inc., forthcoming.
