  Deconstructing a time index into time granularities can assist in exploration
and automated analysis of large temporal data sets. This paper describes
classes of time deconstructions using linear and cyclic time granularities.
Linear granularities respect the linear progression of time such as hours,
days, weeks and months. Cyclic granularities can be circular such as
hour-of-the-day, quasi-circular such as day-of-the-month, and aperiodic such as
public holidays. The hierarchical structure of granularities creates a nested
ordering: hour-of-the-day and second-of-the-minute are single-order-up.
Hour-of-the-week is multiple-order-up, because it passes over day-of-the-week.
Methods are provided for creating all possible granularities for a time index.
A recommendation algorithm provides an indication whether a pair of
granularities can be meaningfully examined together (a "harmony"), or when they
cannot (a "clash").
  Time granularities can be used to create data visualizations to explore for
periodicities, associations and anomalies. The granularities form categorical
variables (ordered or unordered) which induce groupings of the observations.
Assuming a numeric response variable, the resulting graphics are then displays
of distributions compared across combinations of categorical variables.
  The methods implemented in the open source R package `gravitas` are
consistent with a tidy workflow, with probability distributions examined using
the range of graphics available in `ggplot2`.
