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Exercise

Windowed aggregations

data.table's j clause can be used to apply functions to data. Combining by and floor(), we can generate fixed-window aggregates such as "1-day average"!

Windowed aggregations are incredibly valuable in doing data science with time series data. They can be used to downsample high-frequency data so plotting code doesn't make your computer explode.

passengerDT from the previous exercise has been preserved in this session.

In this exercise, we're going to use the "hour_end" indicator in that dataset to generate windowed aggregations.

Instructions 1/3
XP
  • 1
    • Take the mean of "passengers" within each one-hour window. Inspect the output in the console.
  • 2
    • Store the mean number of passengers by hour in a column called "mean_passengers".
  • 3
    • Generate a data.table with the hourly minimum ("min_passengers") and maximum ("max_passengers") passengers.