Trims the date range of a sts object to local time date boundaries which are within the range of data. This has the effect of removing partial-day data records at the start and end of the timeseries and is useful when calculating full-day statistics.

Day boundaries are calculated using the specified timezone or, if NULL, from sts$meta$timezone.

sts_trimDate(sts = NULL, timezone = NULL)

Arguments

sts

SingleTimeSeries sts object.

timezone

Olson timezone used to interpret dates.

Value

A subset of the incoming sts time series object. (A list with meta and data dataframes.)

Examples

library(MazamaTimeSeries)

UTC_week <- sts_filterDate(
  example_sts,
  startdate = 20180808,
  enddate = 20180815,
  timezone = "UTC"
)

# UTC day boundaries
head(UTC_week$data)
#> # A tibble: 6 × 19
#>   datetime            pm25_A pm25_B temperature humidity pressure pm1_atm_A
#>   <dttm>               <dbl>  <dbl>       <dbl>    <dbl>    <dbl>     <dbl>
#> 1 2018-08-08 00:00:00   4.55   4.86          87       29       NA      3.07
#> 2 2018-08-08 00:01:00   4.49   4.43          87       29       NA      3.51
#> 3 2018-08-08 00:02:00   4.38  NA             87       29       NA      3.43
#> 4 2018-08-08 00:03:00  NA      3.93          NA       NA       NA     NA   
#> 5 2018-08-08 00:04:00   4.31   4.66          86       29       NA      3.31
#> 6 2018-08-08 00:05:00   4.86   3.29          86       29       NA      3.86
#> # ℹ 12 more variables: pm25_atm_A <dbl>, pm10_atm_A <dbl>, pm1_atm_B <dbl>,
#> #   pm25_atm_B <dbl>, pm10_atm_B <dbl>, uptime <dbl>, rssi <dbl>, memory <dbl>,
#> #   adc0 <dbl>, bsec_iaq <dbl>, datetime_A <dttm>, datetime_B <dttm>

# Trim to local time day boundaries
local_week <- sts_trimDate(UTC_week)
head(local_week$data)
#> # A tibble: 6 × 19
#>   datetime            pm25_A pm25_B temperature humidity pressure pm1_atm_A
#>   <dttm>               <dbl>  <dbl>       <dbl>    <dbl>    <dbl>     <dbl>
#> 1 2018-08-08 07:00:00  10.2    9             78       41       NA      7.44
#> 2 2018-08-08 07:01:00   9.74   9.21          78       41       NA      7.93
#> 3 2018-08-08 07:02:00   9.56  NA             78       41       NA      7.33
#> 4 2018-08-08 07:03:00  NA      9.14          NA       NA       NA     NA   
#> 5 2018-08-08 07:04:00   9.44   8.55          78       41       NA      7.8 
#> 6 2018-08-08 07:05:00  10      9.66          78       41       NA      7.79
#> # ℹ 12 more variables: pm25_atm_A <dbl>, pm10_atm_A <dbl>, pm1_atm_B <dbl>,
#> #   pm25_atm_B <dbl>, pm10_atm_B <dbl>, uptime <dbl>, rssi <dbl>, memory <dbl>,
#> #   adc0 <dbl>, bsec_iaq <dbl>, datetime_A <dttm>, datetime_B <dttm>