Individual time series in
mts$data are grouped by
unit and then
The most typical use case is creating daily averages where each day begins at
midnight. This function interprets times using the
tzone attribute so be sure that is set properly.
Day boundaries are calculated using the specified
timezone or, if
NULL, the most common (hopefully only!) time zone found in
timezone = NULL, the default,
results in "local time" date filtering which is the most common use case.
mts_summarize( mts, timezone = NULL, unit = c("day", "week", "month", "year"), FUN = NULL, ..., minCount = NULL )
Olson timezone used to interpret dates.
Unit used to summarize by (e.g. "day").
Function used to summarize time series.
Additional arguments to be passed to
na.rm = TRUE).
Minimum number of valid data records required to calculate
summaries. Time periods with fewer valid records will be assigned
An mts time series object containing daily (or other)
(A list with
Because the returned mts object is defined on a daily axis in a
specific time zone, it is important that the incoming
timeseries associated with a single time zone.
library(MazamaTimeSeries) daily <- mts_summarize( mts = Carmel_Valley, timezone = NULL, unit = "day", FUN = mean, na.rm = TRUE, minCount = 18 ) # Daily means head(daily$data) #> # A tibble: 6 × 2 #> datetime a9572a904a4ed46d_840060530002 #> <dttm> <dbl> #> 1 2016-07-22 00:00:00 2.04 #> 2 2016-07-23 00:00:00 6.96 #> 3 2016-07-24 00:00:00 12.4 #> 4 2016-07-25 00:00:00 17.2 #> 5 2016-07-26 00:00:00 64.5 #> 6 2016-07-27 00:00:00 12.8