Release: return current dataframe without changed old rows

This function matches two dataframe objects by their unique identifier (usually "time" or "datetime in a timeseries), and returns a new dataframe which contains the new rows (if present) but matched rows which contain changes from previous data will be dropped.

Usage

release(df_current, df_previous, datetime_variable, include_new = TRUE, ...)

Arguments

df_current

data.frame, the newest/current version of dataset x.

df_previous

data.frame, the old version of dataset, for example x - t1.

datetime_variable

string, which variable to use as unique ID to join df_current and df_previous. Usually a "datetime" variable.

include_new

boolean, should new rows be included? Default is TRUE.

...

Other waldo::compare() arguments can be supplied here, such as tolerance or max_diffs. See ?waldo::compare() for a full list.

Value

A dataframe which contains only rows of df_current that have not changed from df_previous, and includes new rows. Also returns a waldo object as in loupe().

See also

loupe()

create_object_list()

Examples

# Dropping matched rows which contain changes, and returning unchanged rows
df_released <- butterfly::release(
  butterflycount$march, # New or current dataset
  butterflycount$february, # Previous version you are comparing it to
  datetime_variable = "time", # Unique ID variable they have in common
  include_new = TRUE # Whether to include new rows or not, default is TRUE
)
#> The following rows are new in 'df_current': 
#>         time count
#> 1 2024-03-01    23
#> 
#> ℹ The following values have changes from the previous data.
#> old vs new
#>            count
#>   old[1, ]    17
#>   old[2, ]    22
#>   old[3, ]    55
#> - old[4, ]    18
#> + new[4, ]    11
#> 
#> `old$count`: 17.0 22.0 55.0 18.0
#> `new$count`: 17.0 22.0 55.0 11.0
#> 
#> ℹ These will be dropped, but new rows are included.

df_released
#>         time count
#> 1 2024-03-01    23
#> 2 2024-02-01    17
#> 3 2024-01-01    22
#> 4 2023-12-01    55