# The grammar of graphics (L. Wilkinson)

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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.

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