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.

## Usage

tt_time_slice(
tbl,
time_column = NULL,
slice_point = 0,
keep = c("left", "right"),
arrange = FALSE
)

## Arguments

tbl

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

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

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

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

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.

## Value

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 × 11
##    player_id       session_id  session_start       time                item_type
##    <chr>           <chr>       <dttm>              <dttm>              <chr>
##  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
## # … with 1,198 more rows, and 6 more variables: item_name <chr>,
## #   item_revenue <dbl>, session_duration <dbl>, start_day <date>,
## #   acquisition <chr>, country <chr>

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 × 8
##   date_time           date           a b             c     d e     f
##   <dttm>              <date>     <int> <chr>     <dbl> <dbl> <lgl> <chr>
## 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

Other Table Transformers: get_tt_param(), tt_string_info(), tt_summary_stats(), tt_tbl_colnames(), tt_tbl_dims(), tt_time_shift()