Visual Theory, Human Perception and Coding Style

Last time: ggplot

Summary:

• ggplot is plotting package for the future of R computing
• ggplot acts like a grammar - we are able to extend the layers iterative
• ggplot() defines global information for all following elements (relative to data and aes mappings)
• aes allows you to define how to map columns of a data.frame to aesthetics of the graphic
• geom_... define geometric attributes of the graphic
• facets allow one to divide the graphic up conditional on a factor variable

Part I

Visualization Theory

This part lecture is a combination of a blog a wrote a long time ago - that has lots of spelling errors and a compression of common theory that is sometime taught in 315.

Graphics

Why Graphics

• non-trivial patterns
• complex data

When graphics (Maybe better to ask “when not graphics”)

• summaries and tables can be useful for “datset of 20 numbers or less”

~Tufte, The Visual Display of Information, pg 20 and pg 178 (see extra reading for Hw 2)

Overview

Rules to make Good Graphics:

1. Avoid misleading visuals
2. Avoid clutter graphics (make sure your message comes through)
3. Leverage graphics for complex understanding

Human Preception

General dos & don’t for graphics

1. Represent the data as truthfully as possible

1. Truthful Representation

• preserve the size of effect / differences in the data

  Tufte suggests optimizing the Lie Factor: \[\mathrm{Lie\ Factor} = \frac{\mathrm{size\ of\ the\ effect\ in\ the\ graph}}{\mathrm{size\ of\ the\ effect\ in\ the\ data}}\]
• don’t manipulate axes to trick the reader (scaling / truncation is ok if it’s pointed out)
• have standard units
  • $ in fixed units across time
  • rates instead of totals (e.g. GDP vs GDP per cap)

1. Don’t abuse commonly held assumptions