**Vincent Zoonekynd's Blog**, and kindly contributed to R-bloggers)

Though this book is supposed to be a description of the

graphics infrastructure a statistical system could

provide, you can and should also see it as a (huge,

colourful) book of statistical plot examples.

The author suggests to describe a statistical plot in

several consecutive steps: data, transformation, scale,

coordinates, elements, guides, display. The “data” part

performs the actual statistical computations — it has to be

part of the graphics pipeline if you want to be able to

interactively control those computations, say, with a slider

widget. The transformation, scale and coordinate steps,

which I personnally view as a single step, is where most of

the imagination of the plot designer operates: you can

naively plot the data in cartesian coordinates, but you can

also transform it in endless ways, some of which will shed

light on your data (more examples below). The elements are

what is actually plotted (points, lignes, but also

shapes). The guides are the axes, legends and other elements

that help read the plot — for instance, you may have more

than two axes, or plot a priori meaningful lines (say, the

first bissectrix), or complement the title with a picture (a

“thumbnail”). The last step, the display, actually produces

the picture, but should also provide interactivity

(brushing, drill down, zooming, linking, and changes in the

various parameters used in the previous steps).

In the course of the book, the author introduces many

notions linked to actual statistical practice but too

often rejected as being IT problems, such as data mining,

KDD (Knowledge Discovery in Databases); OLAP, ROLAP,

MOLAP, data cube, drill-down, drill-up; data streams;

object-oriented design; design patterns (dynamic plots

are a straightforward example of the “observer pattern”);

eXtreme Programming (XP); Geographical Information

Systems (GIS); XML; perception (e.g., you will learn that

people do not judge quantities and relationships in the

same way after a glance and after lengthy considerations),

etc. — but they are only superficially touched upon,

just enough to wet your apetite.

If you only remember a couple of the topics developped in

the book, these should be: the use of non-cartesian coordinates and,

more generally, data transformations;

scagnostics; data patterns, i.e., the meaningful reordering of

variables and/or observations.

**leave a comment**for the author, please follow the link and comment on their blog:

**Vincent Zoonekynd's Blog**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...