Rowan Trebilco

April 24th 2015

“While graphics technology is moving along at a rapid pace, the human visual system has remained the same.”

– Cleveland (1994) *The elements of graphing data*

examples of new advanced visualisations:

Tufte's rules and general principles

hints for customising plots in R base graphics

- par settings
- approaches for multipanel plotting

“The Visual Display of Quantitative Informaiton” by Edward Tufte (1st edition 1983)

“The Elements of Graphing Data” by William S. Cleveland (1st edition 1985)

“R Graphics 2nd edn.” by Paul Murrell (2011)

Books by Stephen Few and www.perceptualedge.com

Spoon said not to forget to mention: Displaying Time Series, Spatial, and Space-Time Data with R (Lamiguero, 2014)

some great examples of data graphics: http://bost.ocks.org/mike/

**Graphical excellence**

Gives the viewer the greatest number of ideas

in the shortest time

with the least ink

in the smallest space

“Above all else, show the data”

– *The Visual Display of Quantitative Information*

Bad

Better

Even better

This

vs. this

Show data variation, not design variation

Su 2008 Computational Statistics and Data Analysis 52, 4594-4601

Show data variation, not design variation

Su 2008 Computational Statistics and Data Analysis 52, 4594-4601

Chartjunk includes most:

- 3d plots (use colour, size, or contours to show additional dimensions)
- pie charts

… but see Bateman et al. 2010 *“Useful Junk? The Effects of Visual Embellishment on
Comprehension and Memorability of Charts”*

=> chartjunk can be useful for info-graphics?

Maps are are a point of reference for both maximising data-ink and minimising chart-junk

We're good at percieving:

- 45 and 90 degree angles
- lengths
- relative colour and changes in colour intensity

We're bad at percieving:

- angles other than 45 degrees
- areas
- absolute colour and changes in colour hue

**choose a sensible colour scheme**

```
library(RColorBrewer)
display.brewer.all()
```

```
display.brewer.pal(5, "YlOrRd")
```

**choose a sensible colour scheme**

```
library(gplots)
m <- abs(matrix(1:120+rnorm(120), nrow=15, ncol=8))
barplot(m, col=rich.colors(15), main="\nrich.colors")
```

**Carefully consider aspect ratio**