Process multiple days of feeding data and remove outliers using KNN

Process multiple days of feeding data and remove outliers using KNN

Usage

knn_clean_feed(
  feed_data,
  k = 50,
  threshold_percentile = 99.936,
  custom_scaling = NULL,
  intake_col = intake_col2(),
  duration_col = duration_col2(),
  remove_outliers = FALSE,
  date_col = "date"
)

Arguments

feed_data

A list of daily feeding data frames or a single data frame.

k

Integer. Number of nearest neighbors to consider (default: 50). Will be automatically adjusted if it exceeds the number of rows in the data.

threshold_percentile

Numeric. Percentile threshold for outlier detection. Points with average distances above this percentile are considered outliers. Must be between 0 and 100. Default is 99.

custom_scaling

A named list with scaling factors for input variables (e.g., list(rate = 10, intake = 2, duration = 0.5)). If NULL (default), min-max scaling is applied to normalize all variables to a 0-1 range, ensuring equal contribution to distance calculations.

intake_col

Character. Name of the column containing intake data (default: from global_var.R).

duration_col

Character. Name of the column containing duration data (default: from global_var.R).

remove_outliers

Logical. Whether to remove outliers from the data frame.

date_col

Character. Name of the date column if feed_data is a list that needs to be unmerged (default: "date").

Value

If input is a list: a list of data frames with outliers detected. If input is a data frame: a data frame with outliers detected. If remove_outliers=TRUE, returns data with outliers removed.

Details

When custom_scaling is NULL, the function automatically applies min-max scaling to normalize all variables (duration, intake, rate) to a 0-1 range. This ensures equal contribution of each variable to the distance calculation in the KNN algorithm. When custom_scaling is provided, those scaling factors are used instead.