Create a continuous time vector at set time step for a swmpr object
Usage
setstep(dat_in, ...)
# S3 method for class 'swmpr'
setstep(dat_in, timestep = 15, differ = NULL, ...)
# Default S3 method
setstep(dat_in, date_col, timestep = 15, differ = NULL, ...)Arguments
- dat_in
input data object
- ...
arguments passed to or from other methods
- timestep
numeric value of time step to use in minutes. Alternatively, a chr string indicating
'years','quarters','months','days', or'hours'can also be used. A character input assumes 365 days in a year and 31 days in a month.- differ
numeric value defining buffer for merging time stamps to standardized time series, defaults to one half of the timestep
- date_col
chr string for the name of the date/time column, e.g.,
"POSIXct"or"POSIXlt"objects
Details
The setstep function formats a swmpr object to a continuous time series at a given time step. This function is not necessary for most stations but can be useful for combining data or converting an existing time series to a set interval. The first argument of the function, timestep, specifies the desired time step in minutes starting from the nearest hour of the first observation. The second argument, differ, specifies the allowable tolerance in minutes for matching existing observations to user-defined time steps in cases where the two are dissimilar. Values for differ that are greater than one half the value of timestep are not allowed to prevent duplication of existing data. Likewise, the default value for differ is one half the time step. Rows that do not match any existing data within the limits of the differ argument are not discarded. Output from the function can be used with subset and to create a time series at a set interval with empty data removed.
Examples
if (FALSE) { # \dontrun{
## import data
data(apaebmet)
dat <- apaebmet
## convert time series to two hour invervals
## tolerance of +/- 30 minutes for matching existing data
setstep(dat, timestep = 120, differ = 30)
## convert a nutrient time series to a continuous time series
## then remove empty rows and columns
data(apacpnut)
dat_nut <- apacpnut
dat_nut <- setstep(dat_nut, timestep = 60)
subset(dat_nut, rem_rows = TRUE, rem_cols = TRUE)
} # }