utils_new_time: New Time for Time Series Aggregation

In BlasBenito/distantia: Advanced Toolset for Efficient Time Series Dissimilarity Analysis

New Time for Time Series Aggregation

Description

Internal function called by tsl_aggregate() and tsl_resample() to help transform the input argument new_time into the proper format for time series aggregation or resampling.

Usage

utils_new_time(tsl = NULL, new_time = NULL, keywords = "aggregate")

utils_new_time_type(
  tsl = NULL,
  new_time = NULL,
  keywords = c("resample", "aggregate")
)

Arguments

tsl	(required, list) Time series list. Default: NULL
new_time	(required, zoo object, numeric, numeric vector, Date vector, POSIXct vector, or keyword) breakpoints defining aggregation groups. Options are: numeric vector: only for the "numeric" time class, defines the breakpoints for time series aggregation. "Date" or "POSIXct" vector: as above, but for the time classes "Date" and "POSIXct." In any case, the input vector is coerced to the time class of the tsl argument. numeric: defines fixed with time intervals for time series aggregation. Used as is when the time class is "numeric", and coerced to integer and interpreted as days for the time classes "Date" and "POSIXct". keyword (see utils_time_units() and tsl_time_summary()): the common options for the time classes "Date" and "POSIXct" are: "millennia", "centuries", "decades", "years", "quarters", "months", and "weeks". Exclusive keywords for the "POSIXct" time class are: "days", "hours", "minutes", and "seconds". The time class "numeric" accepts keywords coded as scientific numbers, from "1e8" to "1e-8".
keywords	(optional, character string or vector) Defines what keywords are returned. If "aggregate", returns valid keywords for zoo_aggregate(). If "resample", returns valid keywords for zoo_resample(). Default: "aggregate".

Value

Vector of class numeric, Date, or POSIXct

See Also

Other internal_time_handling: utils_as_time(), utils_coerce_time_class(), utils_is_time(), utils_time_keywords(), utils_time_keywords_dictionary(), utils_time_keywords_translate(), utils_time_units()

Examples

#three time series
#climate and ndvi in Fagus sylvatica stands in Spain, Germany, and Sweden
tsl <- tsl_initialize(
  x = fagus_dynamics,
  name_column = "name",
  time_column = "time"
)

# new time for aggregation using keywords
#-----------------------------------

#get valid keywords for aggregation
tsl_time_summary(
  tsl = tsl,
  keywords = "aggregate"
)$keywords

#if no keyword is used, for aggregation the highest resolution keyword is selected automatically
new_time <- utils_new_time(
  tsl = tsl,
  new_time = NULL,
  keywords = "aggregate"
)

new_time

#if no keyword is used
#for resampling a regular version
#of the original time based on the
#average resolution is used instead
new_time <- utils_new_time(
  tsl = tsl,
  new_time = NULL,
  keywords = "resample"
)

new_time

#aggregation time vector form keyword "years"
new_time <- utils_new_time(
  tsl = tsl,
  new_time = "years",
  keywords = "aggregate"
)

new_time

#same from shortened keyword
#see utils_time_keywords_dictionary()
utils_new_time(
  tsl = tsl,
  new_time = "year",
  keywords = "aggregate"
)

#same for abbreviated keyword
utils_new_time(
  tsl = tsl,
  new_time = "y",
  keywords = "aggregate"
)

#from a integer defining a time interval in days
utils_new_time(
  tsl = tsl,
  new_time = 365,
  keywords = "aggregate"
)

#using this vector as input for aggregation
tsl_aggregated <- tsl_aggregate(
  tsl = tsl,
  new_time = new_time
)

BlasBenito/distantia documentation built on Feb. 21, 2025, 2:48 a.m.