Visualizing Patterns over Time - Data Points: Visualization That Means Something - page 93

FIGurE 4-2 Change in job loss from 2001 through 2010
Although you always want to get the big picture, it's also useful to look
at your data in more detail. Are there outliers? Are there any periods of
time that look out of place? Are there spikes or dips? If so, what happened
during that time? Often, these irregularities are where you want to focus.
Other times the outliers can end up being a mistake in data entry. Looking
at the big picture—the context—can help you determine what is what.
Discrete Points in Time
Temporal data can be categorized as discrete or continuous. Knowing which
category your data belongs to can help you decide how to visualize it. In the
discrete case, values are from specific points or blocks of time, and there is
a finite number of possible values. For example, the percentage of people
who pass a test each year is discrete. People take the test, and that's it.
Their scores don't change afterward, and the test is taken on a specific date.
Something like temperature, however, is continuous. It can be measured at
any time of day during any interval, and it is constantly changing.
In this section you look at chart types that help you visualize discrete tem-
poral data, and you see concrete examples on how to create these charts
in R and Illustrator. The beginning will be the main introduction, and then
you can apply the same design patterns throughout the chapter. This part