# Table Transformer: slice a table with a slice point on a time column With any table object containing date, date-time columns, or a mixture thereof, any one of those columns can be used to effectively slice the data table in two with a `slice_point`: and you get to choose which of those slices you want to keep. The slice point can be defined in several ways. One method involves using a decimal value between `0` and `1`, which defines the slice point as the time instant somewhere between the earliest time value (at `0`) and the latest time value (at `1`). Another way of defining the slice point is by supplying a time value, and the following input types are accepted: (1) an ISO 8601 formatted time string (as a date or a date-time), (2) a `POSIXct` time, or (3) a `Date` object. ```r tt_time_slice( tbl, time_column = NULL, slice_point = 0, keep = c("left", "right"), arrange = FALSE ) ``` ## Arguments - `tbl`: **A data table** `obj:` // required A table object to be used as input for the transformation. This can be a data frame, a tibble, a `tbl_dbi` object, or a `tbl_spark` object. - `time_column`: **Column with time data** `scalar` // **default:** `NULL` (`optional`) The time-based column that will be used as a basis for the slicing. If no time column is provided then the first one found will be used. - `slice_point`: `scalar` // **default:** `0` The location on the `time_column` where the slicing will occur. This can either be a decimal value from `0` to `1`, an ISO 8601 formatted time string (as a date or a date-time), a `POSIXct` time, or a `Date` object. - `keep`: **Data slice to keep** `singl-kw:[left|right]` // **default:** `"left"` Which slice should be kept? The `"left"` side (the default) contains data rows that are earlier than the `slice_point` and the `"right"` side will have rows that are later. - `arrange`: **Arrange data slice by the time data?** `scalar` // **default:** `FALSE` Should the slice be arranged by the `time_column`? This may be useful if the input `tbl` isn't ordered by the `time_column`. By default, this is `FALSE` and the original ordering is retained. ## Returns A data frame, a tibble, a `tbl_dbi` object, or a `tbl_spark` object depending on what was provided as `tbl`. ## Details There is the option to `arrange` the table by the date or date-time values in the `time_column`. This ordering is always done in an ascending manner. Any `NA`/`NULL` values in the `time_column` will result in the corresponding rows can being removed (no matter which slice is retained). ## Examples Let's use the `game_revenue` dataset, included in the pointblank package, as the input table for the first demo. It has entries in the first 21 days of 2015 and we'll elect to get all of the records where the `time` values are strictly for the first 15 days of 2015. The `keep` argument has a default of `"left"` so all rows where the `time` column is less than `"2015-01-16 00:00:00"` will be kept. ``` tt_time_slice( tbl = game_revenue, time_column = "time", slice_point = "2015-01-16" ) #> # A tibble: 1,208 x 11 #> player_id session_id session_start time item_type #> #> 1 ECPANOIXLZHF896 ECPANOIXLZ~ 2015-01-01 01:31:03 2015-01-01 01:31:27 iap #> 2 ECPANOIXLZHF896 ECPANOIXLZ~ 2015-01-01 01:31:03 2015-01-01 01:36:57 iap #> 3 ECPANOIXLZHF896 ECPANOIXLZ~ 2015-01-01 01:31:03 2015-01-01 01:37:45 iap #> 4 ECPANOIXLZHF896 ECPANOIXLZ~ 2015-01-01 01:31:03 2015-01-01 01:42:33 ad #> 5 ECPANOIXLZHF896 ECPANOIXLZ~ 2015-01-01 11:50:02 2015-01-01 11:55:20 ad #> 6 ECPANOIXLZHF896 ECPANOIXLZ~ 2015-01-01 11:50:02 2015-01-01 12:08:56 ad #> 7 ECPANOIXLZHF896 ECPANOIXLZ~ 2015-01-01 11:50:02 2015-01-01 12:14:08 ad #> 8 ECPANOIXLZHF896 ECPANOIXLZ~ 2015-01-01 11:50:02 2015-01-01 12:21:44 ad #> 9 ECPANOIXLZHF896 ECPANOIXLZ~ 2015-01-01 11:50:02 2015-01-01 12:24:20 ad #> 10 FXWUORGYNJAE271 FXWUORGYNJ~ 2015-01-01 15:17:18 2015-01-01 15:19:36 ad #> # i 1,198 more rows #> # i 6 more variables: item_name , item_revenue , #> # session_duration , start_day , acquisition , country ``` Omit the first 25% of records from `small_table`, also included in the package, with a fractional `slice_point` of `0.25` on the basis of a timeline that begins at `2016-01-04 11:00:00` and ends at `2016-01-30 11:23:00`. ``` small_table %>% tt_time_slice( slice_point = 0.25, keep = "right" ) #> # A tibble: 8 x 8 #> date_time date a b c d e f #> #> 1 2016-01-11 06:15:00 2016-01-11 4 2-dhe-923 4 3291. TRUE mid #> 2 2016-01-15 18:46:00 2016-01-15 7 1-knw-093 3 843. TRUE high #> 3 2016-01-17 11:27:00 2016-01-17 4 5-boe-639 2 1036. FALSE low #> 4 2016-01-20 04:30:00 2016-01-20 3 5-bce-642 9 838. FALSE high #> 5 2016-01-20 04:30:00 2016-01-20 3 5-bce-642 9 838. FALSE high #> 6 2016-01-26 20:07:00 2016-01-26 4 2-dmx-010 7 834. TRUE low #> 7 2016-01-28 02:51:00 2016-01-28 2 7-dmx-010 8 108. FALSE low #> 8 2016-01-30 11:23:00 2016-01-30 1 3-dka-303 NA 2230. TRUE high ``` ## Function ID 12-6 ## See Also Other Table Transformers: `get_tt_param()`, `tt_string_info()`, `tt_summary_stats()`, `tt_tbl_colnames()`, `tt_tbl_dims()`, `tt_time_shift()`

tt_time_slice function