Exercise

# Temporal attributes and decimal dates

The temporal attributes of a time series – start, end, and frequency – are valuable in understanding how the time series was sampled.

For example, the start and end points help determine the duration of your data. The frequency determines how often the data were sampled. A time series regularly sampled each month would have a frequency of `12`

; there are 12 months per year.

Often, dates are stored as *decimal dates* (e.g., `2019.125`

), which are useful when the time series was sampled, for example, exactly 100 times in a year.

Converting decimal dates to human-legible dates is an important step in time series analysis, especially when you're presenting data to others.

The `maunaloa`

time series and the `zoo`

and `lubridate`

packages are available to you.

Instructions 1/2

## Question

Using the console, determine the sampling frequency of the `maunaloa`

dataset.