timetools-package: Seasonal/Sequential (Instants/Durations, Even or not) Time...

In timetools: Seasonal/Sequential (Instants/Durations, Even or not) Time Series

Seasonal/Sequential (Instants/Durations, Even or not) Time Series

Description

Objects to manipulate sequential and seasonal time series. Sequential time series based on time instants and time durations are handled. Both can be regularly or unevenly spaced (overlapping durations are allowed).

Only POSIX* format are used for dates and times.

The following classes are provided : POSIXcti, POSIXctp, TimeIntervalDataFrame, TimeInstantDataFrame, SubtimeDataFrame ; methods to switch from a class to another and to modify the time support of series (hourly time series to daily time series for instance) are also defined.

Tools provided can be used for instance to handle environmental monitoring data (not always produced on a regular time base).

Author(s)

Vladislav Navel <[e-mail]>

See Also

TimeInstantDataFrame, TimeIntervalDataFrame, changeSupport, SubtimeDataFrame, POSIXcti, POSIXctp

Examples

ti1 <- TimeIntervalDataFrame (
	c('2010-01-01', '2010-02-01'), c('2010-02-01', '2010-02-02'),
	'UTC', data.frame(ex1=1:2) )

ti2 <- TimeIntervalDataFrame (
	c('2010-01-01', '2010-02-01', '2010-02-02'), NULL,
	'UTC', data.frame(ex1=1:2) )

all.equal (ti1, ti2)

ti3 <- TimeIntervalDataFrame (
	c('2010-01-01', '2010-01-02', '2010-01-04'), NULL,
	'UTC', data.frame(ex3=c(6, 1.5)))

# weighted mean over a period of 3 days with at least 75% of
# coverage (NA is retunr if not)
ti3
d <- POSIXctp(unit='day')
changeSupport (ti3, 3L*d, 0.75)


ti4 <- TimeIntervalDataFrame (
	c('2010-01-01', '2010-01-02', '2010-01-04', 
	  '2010-01-07', '2010-01-09', '2010-01-10'), NULL,
	'UTC', data.frame(ex4=c(6, 1.5, 5, 3, NA)))

# weighted mean over a period of 3 days with at least 75% of
# coverage (NA is retunr if not) or 50%
ti4
changeSupport (ti4, 3L*d, 0.75)
changeSupport (ti4, 3L*d, 0.5)

timetools documentation built on Aug. 29, 2023, 5:09 p.m.