timeline_group: timeline_group: check if a timeseries is continuous

In butterfly: Verification for Continually Updating Time Series Data

timeline_group: check if a timeseries is continuous

Description

If after using timeline() you have established a timeseries is not continuous, or if you are working with data where you expect distinct sequences or events, you can use timeline_group() to extract and classify different distinct continuous chunks of your data.

Usage

timeline_group(df_current, datetime_variable, expected_lag = 1)

Arguments

df_current	data.frame, the newest/current version of dataset x.
datetime_variable	string, the "datetime" variable that should be checked for continuity.
expected_lag	numeric, the acceptable difference between timestep for a timeseries to be classed as continuous. Any difference greater than expected_lag will indicate a timeseries is not continuous. Default is 1. The smallest units of measurement present in the column will be used. In a column formatted YYYY-MM-DD day will be used, therefore 1 would be 1 day, 7 would be a week.

Details

We attempt to do this without sorting, or changing the data for a couple of reasons:

1. There are no difference in dates: Some instruments might record dates that appear identical, but are still in chronological order. For example, high-frequency data in fractional seconds. This is a rare use case though.

2. Dates are generally ascending/descending, but the instrument has returned to origin. Probably more common, and will results in a non-continuous dataset, however the records are still in chronological order This is something we would like to discover. This is accounted for in the logic in case_when().

Note: for monthly data it is recommended you convert your Date column to a monthly format (e.g 2024-October, 10-2024, Oct-2024 etc.), so a constant expected lag can be set (not a range of 29 - 31 days).

Value

A data.frame, identical to df_current, but with extra columns timeline_group, which assigns a number to each continuous sets of data and timelag which specifies the time lags between rows.

Examples

# A nice continuous dataset should return TRUE
# In February, our imaginary rain gauge's onboard computer had a failure.
# The timestamp was reset to 1970-01-01

# We want to group these different distinct continuous sequences:
butterfly::timeline_group(
  forestprecipitation$february,
  datetime_variable = "time",
  expected_lag = 1
)

butterfly documentation built on April 12, 2025, 9:10 a.m.