| hs_tstep {htsr} | R Documentation |
hts time series with fixed timestep
Description
Computes time-series with a fixed timestep from infra-daily to monthly within a shiny web page.
Usage
hs_tstep()
Details
First of all, one must select a "starting" hts file, instantaneous or already with a fixed timestep.
Then one must choose the computing time-step and mode, between the possible choices. Note that the timezone considered is the timezone of the "starting" file.
Possible time-steps are: 5, 10 or 30 minutes, 1, 2, 3, 6 or 12 hours, 1 day, 1 month. It shall be noted that when computing the monthly time step, the daily time step is previously computed.
Possible modes are: average, sum, max or min. For monthly time step, max and min offers two options: daily max averages, respectively min, or absolute, respectively min.
In the case of a daily timestep, a shift value (in hours) allows to shift the time interval. For example if shift = 6, the date is computed from 6am until 6am the following day. The result is dated in the middle of the interval, i.e. if shift = 6; the datetime is 18.
In the case of a monthly timestep, associated additional time series can be optionally computed:
A mean monthly climatology, taking into account or not the missing daily values with the option "remove NA". Climatology files are by convention awarded to year 2000.
Excel files: with a calendar presentation (days in rows, months in columns, years in sheets): option caledit_j ; with the monthly means (or sums): option caledit_m.
Missing values can be replaced by the mean of the existing values for other years: option gapfill.
Extract year stat
The output files are written in same folder as the starting hts file.
Value
hts files at the requested timestep with a suffix giving the timestep in minutes, i.e. 1440 for the daily timestep. In the case or monthly timestep, the suffixes are: M for the current case, C for the climatology, G for the gapfilled file.
Optionally, two Excel files with values in "calendar form": one with daily data and one with monthly data, the fist one with a ad_ prefix and the second one with the am_ prefix.
Author(s)
P. Chevallier - Oct 2017 - Sep 2023