This shorthand respects time zones and encourages compact expressions.
Arguments
- .data
A tsibble.
- ...
Formulas that specify start and end periods (inclusive), or strings.
~ endor. ~ end: from the very beginning to a specified ending period.start ~ end: from specified beginning to ending periods.start ~ .: from a specified beginning to the very end of the data. Supported index type:POSIXct(to seconds),Date,yearweek,yearmonth/yearmon,yearquarter/yearqtr,hms/difftime&numeric.
- .preserve
Relevant when the
.datainput is grouped. If.preserve = FALSE(the default), the grouping structure is recalculated based on the resulting data, otherwise the grouping is kept as is.
System Time Zone ("Europe/London")
There is a known issue of an extra hour gained for a machine setting time
zone to "Europe/London", regardless of the time zone associated with
the POSIXct inputs. It relates to anytime and Boost. Use Sys.timezone()
to check if the system time zone is "Europe/London". It would be recommended to
change the global environment "TZ" to other equivalent names: GB, GB-Eire,
Europe/Belfast, Europe/Guernsey, Europe/Isle_of_Man and Europe/Jersey as
documented in ?Sys.timezone(), using Sys.setenv(TZ = "GB") for example.
See also
time_in for a vector of time index
Examples
# from the starting time to the end of Feb, 2015
pedestrian %>%
filter_index(~ "2015-02")
#> # A tsibble: 4,536 x 5 [1h] <Australia/Melbourne>
#> # Key: Sensor [4]
#> Sensor Date_Time Date Time Count
#> <chr> <dttm> <date> <int> <int>
#> 1 Birrarung Marr 2015-01-01 00:00:00 2015-01-01 0 1630
#> 2 Birrarung Marr 2015-01-01 01:00:00 2015-01-01 1 826
#> 3 Birrarung Marr 2015-01-01 02:00:00 2015-01-01 2 567
#> 4 Birrarung Marr 2015-01-01 03:00:00 2015-01-01 3 264
#> 5 Birrarung Marr 2015-01-01 04:00:00 2015-01-01 4 139
#> 6 Birrarung Marr 2015-01-01 05:00:00 2015-01-01 5 77
#> 7 Birrarung Marr 2015-01-01 06:00:00 2015-01-01 6 44
#> 8 Birrarung Marr 2015-01-01 07:00:00 2015-01-01 7 56
#> 9 Birrarung Marr 2015-01-01 08:00:00 2015-01-01 8 113
#> 10 Birrarung Marr 2015-01-01 09:00:00 2015-01-01 9 166
#> # ℹ 4,526 more rows
# entire Feb 2015, & from the beginning of Aug 2016 to the end
pedestrian %>%
filter_index("2015-02", "2016-08" ~ .)
#> # A tsibble: 16,244 x 5 [1h] <Australia/Melbourne>
#> # Key: Sensor [4]
#> Sensor Date_Time Date Time Count
#> <chr> <dttm> <date> <int> <int>
#> 1 Birrarung Marr 2015-02-01 00:00:00 2015-02-01 0 178
#> 2 Birrarung Marr 2015-02-01 01:00:00 2015-02-01 1 39
#> 3 Birrarung Marr 2015-02-01 02:00:00 2015-02-01 2 41
#> 4 Birrarung Marr 2015-02-01 03:00:00 2015-02-01 3 32
#> 5 Birrarung Marr 2015-02-01 04:00:00 2015-02-01 4 33
#> 6 Birrarung Marr 2015-02-01 05:00:00 2015-02-01 5 39
#> 7 Birrarung Marr 2015-02-01 06:00:00 2015-02-01 6 45
#> 8 Birrarung Marr 2015-02-01 07:00:00 2015-02-01 7 45
#> 9 Birrarung Marr 2015-02-01 08:00:00 2015-02-01 8 96
#> 10 Birrarung Marr 2015-02-01 09:00:00 2015-02-01 9 116
#> # ℹ 16,234 more rows
# multiple time windows
pedestrian %>%
filter_index(~"2015-02", "2015-08" ~ "2015-09", "2015-12" ~ "2016-02")
#> # A tsibble: 19,008 x 5 [1h] <Australia/Melbourne>
#> # Key: Sensor [4]
#> Sensor Date_Time Date Time Count
#> <chr> <dttm> <date> <int> <int>
#> 1 Birrarung Marr 2015-01-01 00:00:00 2015-01-01 0 1630
#> 2 Birrarung Marr 2015-01-01 01:00:00 2015-01-01 1 826
#> 3 Birrarung Marr 2015-01-01 02:00:00 2015-01-01 2 567
#> 4 Birrarung Marr 2015-01-01 03:00:00 2015-01-01 3 264
#> 5 Birrarung Marr 2015-01-01 04:00:00 2015-01-01 4 139
#> 6 Birrarung Marr 2015-01-01 05:00:00 2015-01-01 5 77
#> 7 Birrarung Marr 2015-01-01 06:00:00 2015-01-01 6 44
#> 8 Birrarung Marr 2015-01-01 07:00:00 2015-01-01 7 56
#> 9 Birrarung Marr 2015-01-01 08:00:00 2015-01-01 8 113
#> 10 Birrarung Marr 2015-01-01 09:00:00 2015-01-01 9 166
#> # ℹ 18,998 more rows
# entire 2015
pedestrian %>%
filter_index("2015")
#> # A tsibble: 32,276 x 5 [1h] <Australia/Melbourne>
#> # Key: Sensor [4]
#> Sensor Date_Time Date Time Count
#> <chr> <dttm> <date> <int> <int>
#> 1 Birrarung Marr 2015-01-01 00:00:00 2015-01-01 0 1630
#> 2 Birrarung Marr 2015-01-01 01:00:00 2015-01-01 1 826
#> 3 Birrarung Marr 2015-01-01 02:00:00 2015-01-01 2 567
#> 4 Birrarung Marr 2015-01-01 03:00:00 2015-01-01 3 264
#> 5 Birrarung Marr 2015-01-01 04:00:00 2015-01-01 4 139
#> 6 Birrarung Marr 2015-01-01 05:00:00 2015-01-01 5 77
#> 7 Birrarung Marr 2015-01-01 06:00:00 2015-01-01 6 44
#> 8 Birrarung Marr 2015-01-01 07:00:00 2015-01-01 7 56
#> 9 Birrarung Marr 2015-01-01 08:00:00 2015-01-01 8 113
#> 10 Birrarung Marr 2015-01-01 09:00:00 2015-01-01 9 166
#> # ℹ 32,266 more rows
# specific
pedestrian %>%
filter_index("2015-03-23" ~ "2015-10")
#> # A tsibble: 20,180 x 5 [1h] <Australia/Melbourne>
#> # Key: Sensor [4]
#> Sensor Date_Time Date Time Count
#> <chr> <dttm> <date> <int> <int>
#> 1 Birrarung Marr 2015-03-23 00:00:00 2015-03-23 0 39
#> 2 Birrarung Marr 2015-03-23 01:00:00 2015-03-23 1 24
#> 3 Birrarung Marr 2015-03-23 02:00:00 2015-03-23 2 1
#> 4 Birrarung Marr 2015-03-23 03:00:00 2015-03-23 3 3
#> 5 Birrarung Marr 2015-03-23 04:00:00 2015-03-23 4 16
#> 6 Birrarung Marr 2015-03-23 05:00:00 2015-03-23 5 36
#> 7 Birrarung Marr 2015-03-23 06:00:00 2015-03-23 6 178
#> 8 Birrarung Marr 2015-03-23 07:00:00 2015-03-23 7 462
#> 9 Birrarung Marr 2015-03-23 08:00:00 2015-03-23 8 756
#> 10 Birrarung Marr 2015-03-23 09:00:00 2015-03-23 9 289
#> # ℹ 20,170 more rows
pedestrian %>%
filter_index("2015-03-23" ~ "2015-10-31")
#> # A tsibble: 20,180 x 5 [1h] <Australia/Melbourne>
#> # Key: Sensor [4]
#> Sensor Date_Time Date Time Count
#> <chr> <dttm> <date> <int> <int>
#> 1 Birrarung Marr 2015-03-23 00:00:00 2015-03-23 0 39
#> 2 Birrarung Marr 2015-03-23 01:00:00 2015-03-23 1 24
#> 3 Birrarung Marr 2015-03-23 02:00:00 2015-03-23 2 1
#> 4 Birrarung Marr 2015-03-23 03:00:00 2015-03-23 3 3
#> 5 Birrarung Marr 2015-03-23 04:00:00 2015-03-23 4 16
#> 6 Birrarung Marr 2015-03-23 05:00:00 2015-03-23 5 36
#> 7 Birrarung Marr 2015-03-23 06:00:00 2015-03-23 6 178
#> 8 Birrarung Marr 2015-03-23 07:00:00 2015-03-23 7 462
#> 9 Birrarung Marr 2015-03-23 08:00:00 2015-03-23 8 756
#> 10 Birrarung Marr 2015-03-23 09:00:00 2015-03-23 9 289
#> # ℹ 20,170 more rows
pedestrian %>%
filter_index("2015-03-23 10" ~ "2015-10-31 12")
#> # A tsibble: 20,107 x 5 [1h] <Australia/Melbourne>
#> # Key: Sensor [4]
#> Sensor Date_Time Date Time Count
#> <chr> <dttm> <date> <int> <int>
#> 1 Birrarung Marr 2015-03-23 10:00:00 2015-03-23 10 199
#> 2 Birrarung Marr 2015-03-23 11:00:00 2015-03-23 11 120
#> 3 Birrarung Marr 2015-03-23 12:00:00 2015-03-23 12 317
#> 4 Birrarung Marr 2015-03-23 13:00:00 2015-03-23 13 583
#> 5 Birrarung Marr 2015-03-23 14:00:00 2015-03-23 14 265
#> 6 Birrarung Marr 2015-03-23 15:00:00 2015-03-23 15 275
#> 7 Birrarung Marr 2015-03-23 16:00:00 2015-03-23 16 409
#> 8 Birrarung Marr 2015-03-23 17:00:00 2015-03-23 17 698
#> 9 Birrarung Marr 2015-03-23 18:00:00 2015-03-23 18 546
#> 10 Birrarung Marr 2015-03-23 19:00:00 2015-03-23 19 276
#> # ℹ 20,097 more rows