  Temporal data is information measured in the context of time. This contextual
structure provides components that need to be explored to understand the data
and that can form the basis of interactions applied to the plots. In
multivariate time series we expect to see temporal dependence, long term and
seasonal trends and cross-correlations. In longitudinal data we also expect
within and between subject dependence. Time series and longitudinal data,
although analyzed differently, are often plotted using similar displays. We
provide a taxonomy of interactions on plots that can enable exploring temporal
components of these data types, and describe how to build these interactions
using data transformations. Because temporal data is often accompanied other
types of data we also describe how to link the temporal plots with other
displays of data. The ideas are conceptualized into a data pipeline for
temporal data, and implemented into the R package cranvas. This package
provides many different types of interactive graphics that can be used together
to explore data or diagnose a model fit.
