butterfly (version 1.1.2)

timeline: timeline: check if a timeseries is continuous

Description

Check if a timeseries is continuous. Even if a timeseries does not contain obvious gaps, this does not automatically mean it is also continuous.

Usage

timeline(df_current, datetime_variable, expected_lag = 1)

Value

A boolean, TRUE if the timeseries is continuous, and FALSE if there are more than one continuous timeseries within the dataset.

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

Measuring instruments can have different behaviours when they fail. For example, during power failure an internal clock could reset to "1970-01-01", or the manufacturing date (say, "2021-01-01"). This leads to unpredictable ways of checking if a dataset is continuous.

The timeline_group() and timeline() functions attempt to give the user control over how to check for continuity by providing an expected_lag. The difference between timesteps in a dataset should not exceed the expected_lag.

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).

See Also

timeline_group()

Examples

# A nice continuous dataset should return TRUE
butterfly::timeline(
  forestprecipitation$january,
  datetime_variable = "time",
  expected_lag = 1
)

# In February, our imaginary rain gauge's onboard computer had a failure.
# The timestamp was reset to 1970-01-01
butterfly::timeline(
  forestprecipitation$february,
  datetime_variable = "time",
  expected_lag = 1
)