Merge meal clustering results with original visit data

Merges the meal-level results from cluster_meals() with the original feeding visit data to assign meal information to each individual visit. Visits not assigned to any meal (outliers) are labeled with meal_id = 0.

Usage

merge_cluster_results(
  visit_data,
  meal_results,
  id_col = id_col2(),
  start_col = start_col2(),
  end_col = end_col2(),
  tz = tz2()
)

Arguments

visit_data

Original feeding visit data (dataframe or list of dataframes)

meal_results

Results from cluster_meals() function

id_col

Animal ID column name (default current global value from id_col2())

start_col

Start time column name (default current global value from start_col2())

end_col

End time column name (default current global value from end_col2())

tz

Timezone (default current global value from tz2())

Value

Same structure as input visit_data (dataframe or list of dataframes) with original visit data plus meal information columns:

meal_id

Sequential meal number within animal-day (0 for outliers)

meal_start

Start time of the meal this visit belongs to (NA for outliers)

meal_end

End time of the meal this visit belongs to (NA for outliers)

meal_duration

Total duration of the meal this visit belongs to (NA for outliers)

total_intake

Total intake of the meal this visit belongs to (NA for outliers)

visit_count

Number of visits in the meal this visit belongs to (NA for outliers)

Details

The function matches visits to meals based on animal ID, date, and whether the visit time falls within the meal's start and end times. Visits that don't match any meal are considered outliers and assigned meal_id = 0.

Examples

# Cluster meals first (all_fed is included in the package)
meals <- cluster_meals(all_fed[[1]])

# Merge results with original data
merged_data <- merge_cluster_results(all_fed[[1]], meals)

# Check how many visits were assigned vs. outliers
table(merged_data$meal_id == 0)
#> 
#> FALSE  TRUE 
#>  3477     6