Force data into predefined time intervals

Source: R/assess_continuousTimeIntervals.R

The measurements are fitted into timesteps defined be the first point of time and a temporal resolution

continuousTimeIntervals(
  time_vector,
  data_vector,
  res = 15,
  first_pointOfTime = min(time_vector, na.rm = TRUE),
  last_pointOfTime = max(time_vector, na.rm = TRUE)
)

Arguments

time_vector

A POSIXct vector

data_vector

A numeric vector, with data corresponding to the time_vector

res

Temporal resolution in minutes

first_pointOfTime

Starting point (POSIXct) of the newly defined time series. By default the minimum of the time_vector

last_pointOfTime

End point (POSIXct) of the newly defined time series. By default the maximum of the time_vector

Value

Dataframe with POSIX column "t" and data column "d"

Details

In a first step a vactor is generated with continuous timesteps, starting at first_pointOfTime by a defined time interval. Subsequently, the measured data is forced into timesteps with a similar time interval. Here, the measurements are assigned to the timestep that is closest to the actual time of measurements. If more than one measurement are assigned to one timestep, the average is used. If there is no measurement, NA is used.