Graphics Reference
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ations using an equal-sized grid to plot data are same bin size, fluctuation diagrams,
and multiple barcharts.
Figure . shows a mosaicplot in multiple multiple-barchart view with splitting
directions x, y, x, x. he way the information is plotted is exactly the same as in
Figs. . and . . Flexible implementations of mosaicplots offering these variations
can be found in Mondrian (heus, ) and MANET (Unwin et al., ).
Trellis Displays and Interactivity
6.3.3
he conditional framework in a trellis display can be regarded as static snapshots of
interactive statistical graphics. he single view in a panel of a trellis display can also
be thought of as the highlighted part of the graphics of the panel plot for the con-
ditioned subgroup. his can be best illustrated by looking at the cars data set again.
Figure . shows a screenshot of an interactive session. Selecting a specific subgroup
in a barchart or mosaicplot is one interaction. Another interaction would be brush-
ing.Brushingaplotmeans tosteadilymoveabrush,i.e.,anindicatorfortheselection
region, along one or two axes of a plot. he selected interval from the brush can be
seen as an interval of a shingle variable. When a continuous variable is subdivided
into,e.g.,five intervals, thiscorrespondstofive snapshots ofthecontinuous brushing
process from the minimum to the maximum of that variable. For the same scatter-
plot shown in Fig. . , Fig. . shows a snapshot of a brush selecting the lowest val-
uesof the conditioning variables Engine Size and Horsepower. Now the motivation of
shingle variables is moreobvious, as they relate directly to this interactive technique.
Brushing with linked highlighting is certainly far more flexible than the static view
in a trellis display. On the other hand, the trellis display can easily be reproduced in
printed form, which is impossible for the interactive process of brushing.
Figure . . Selecting the group of front-wheel-drive sedans in the mosaicplot in multiple-barchart view
(let), one gets the corresponding panel plot (scatterplot on right) from Fig. . in the highlighted
subgroup
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