Catch: return dataframe containing only rows that have changed

This function matches two dataframe objects by their unique identifier (usually "time" or "datetime in a timeseries), and returns a new dataframe which contains only rows that have changed compared to previous data. It will not return any new rows.

Usage

catch(df_current, df_previous, datetime_variable, ...)

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.
...: 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 changes from df_previous, but without new rows. Also returns a waldo object as in loupe().

Details

The underlying functionality is handled by create_object_list().

Examples

# Returning only matched rows which contain changes
df_caught <- butterfly::catch(
  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
)
#> 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 Only these rows are returned.

df_caught
#>         time count
#> 1 2023-11-01    18