R: Table Transformer: shift the times of a table

tt_time_shift {pointblank}

R Documentation

Table Transformer: shift the times of a table

Description

With any table object containing date or date-time columns, these values can be precisely shifted with tt_time_shift() and specification of the time shift. We can either provide a string with the time shift components and the shift direction (like "-4y 10d") or a difftime object (which can be created via lubridate expressions or by using the base::difftime() function).

Usage

tt_time_shift(tbl, time_shift = "0y 0m 0d 0H 0M 0S")

Arguments

tbl

A data table

⁠obj:<tbl_*>⁠ // 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_shift

Time-shift specification

⁠scalar<character>⁠ // default: "0y 0m 0d 0H 0M 0S"

Either a character-based representation that specifies the time difference by which all time values in time-based columns will be shifted, or, a difftime object. The character string is constructed in the format "0y 0m 0d 0H 0M 0S" and individual time components can be omitted (i.e., "1y 5d" is a valid specification of shifting time values ahead one year and five days). Adding a "-" at the beginning of the string (e.g., "-2y") will shift time values back.

Details

The time_shift specification cannot have a higher time granularity than the least granular time column in the input table. Put in simpler terms, if there are any date-based based columns (or just a single date-based column) then the time shifting can only be in terms of years, months, and days. Using a time_shift specification of "20d 6H" in the presence of any dates will result in a truncation to "20d". Similarly, a difftime object will be altered in the same circumstances, however, the object will resolved to an exact number of days through rounding.

Value

A data frame, a tibble, a tbl_dbi object, or a tbl_spark object depending on what was provided as tbl.

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 move all of the date and date-time values to the beginning of 2021 with the tt_time_shift() function and the "6y" time_shift specification.

tt_time_shift(
  tbl = game_revenue,
  time_shift = "6y"
)
#> # A tibble: 2,000 x 11
#>    player_id       session_id  session_start       time                item_type
#>    <chr>           <chr>       <dttm>              <dttm>              <chr>    
#>  1 ECPANOIXLZHF896 ECPANOIXLZ~ 2021-01-01 01:31:03 2021-01-01 01:31:27 iap      
#>  2 ECPANOIXLZHF896 ECPANOIXLZ~ 2021-01-01 01:31:03 2021-01-01 01:36:57 iap      
#>  3 ECPANOIXLZHF896 ECPANOIXLZ~ 2021-01-01 01:31:03 2021-01-01 01:37:45 iap      
#>  4 ECPANOIXLZHF896 ECPANOIXLZ~ 2021-01-01 01:31:03 2021-01-01 01:42:33 ad       
#>  5 ECPANOIXLZHF896 ECPANOIXLZ~ 2021-01-01 11:50:02 2021-01-01 11:55:20 ad       
#>  6 ECPANOIXLZHF896 ECPANOIXLZ~ 2021-01-01 11:50:02 2021-01-01 12:08:56 ad       
#>  7 ECPANOIXLZHF896 ECPANOIXLZ~ 2021-01-01 11:50:02 2021-01-01 12:14:08 ad       
#>  8 ECPANOIXLZHF896 ECPANOIXLZ~ 2021-01-01 11:50:02 2021-01-01 12:21:44 ad       
#>  9 ECPANOIXLZHF896 ECPANOIXLZ~ 2021-01-01 11:50:02 2021-01-01 12:24:20 ad       
#> 10 FXWUORGYNJAE271 FXWUORGYNJ~ 2021-01-01 15:17:18 2021-01-01 15:19:36 ad       
#> # i 1,990 more rows
#> # i 6 more variables: item_name <chr>, item_revenue <dbl>,
#> #   session_duration <dbl>, start_day <date>, acquisition <chr>, country <chr>

Keeping only the date_time and a-f columns of small_table, also included in the package, shift the times back 2 days and 12 hours with the "-2d 12H" specification.

small_table %>%
  dplyr::select(-date) %>%
  tt_time_shift("-2d 12H")
#> # A tibble: 13 x 7
#>    date_time               a b             c      d e     f    
#>    <dttm>              <int> <chr>     <dbl>  <dbl> <lgl> <chr>
#>  1 2016-01-01 23:00:00     2 1-bcd-345     3  3423. TRUE  high 
#>  2 2016-01-01 12:32:00     3 5-egh-163     8 10000. TRUE  low  
#>  3 2016-01-03 01:32:00     6 8-kdg-938     3  2343. TRUE  high 
#>  4 2016-01-04 05:23:00     2 5-jdo-903    NA  3892. FALSE mid  
#>  5 2016-01-07 00:36:00     8 3-ldm-038     7   284. TRUE  low  
#>  6 2016-01-08 18:15:00     4 2-dhe-923     4  3291. TRUE  mid  
#>  7 2016-01-13 06:46:00     7 1-knw-093     3   843. TRUE  high 
#>  8 2016-01-14 23:27:00     4 5-boe-639     2  1036. FALSE low  
#>  9 2016-01-17 16:30:00     3 5-bce-642     9   838. FALSE high 
#> 10 2016-01-17 16:30:00     3 5-bce-642     9   838. FALSE high 
#> 11 2016-01-24 08:07:00     4 2-dmx-010     7   834. TRUE  low  
#> 12 2016-01-25 14:51:00     2 7-dmx-010     8   108. FALSE low  
#> 13 2016-01-27 23:23:00     1 3-dka-303    NA  2230. TRUE  high

Function ID

12-5