Calculate Percentage of Feed Remaining at Each Visit

Calculates the percentage of feed remaining at each visit based on the most recent feed addition for that specific bin. Requires per-bin feed addition data from detect_feed_additions(). Returns both visit-level data with feed percentages and daily summary statistics.

Usage

calculate_feed_availability(
  visit_data,
  feed_addition_data,
  id_col = id_col2(),
  bin_col = bin_col2(),
  start_col = start_col2(),
  start_weight_col = start_weight_col2()
)

Arguments

visit_data

A named list of daily data frames or a single data frame containing visit records.

feed_addition_data

A named list of daily data frames or a single data frame containing feed addition events from detect_feed_additions(). Must have aggregate_all_bin = FALSE to get per-bin feed additions.

id_col

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

bin_col

Bin ID column name (default current global value from bin_col2())

start_col

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

start_weight_col

Start weight column name (default current global value from start_weight_col2())

Value

A list with two elements:

• visits - Visit-level data with added columns:

  • feed_addition_time - Time when feed was added to this bin

  • feed_added_weight - Weight of feed added to this bin (kg)

  • pct_feed_remaining - Percentage of feed remaining at visit start

• daily_summary - Named list (or data frame) with columns:

  • date - Date

  • [id_col] - Animal identifier

  • mean_pct_feed_remaining - Average percentage across visits

  • median_pct_feed_remaining - Median percentage across visits

  • sd_pct_feed_remaining - Standard deviation of percentage

  • total_visits_analyzed - Number of visits analyzed

Details

The function matches each visit to the most recent feed addition for that specific bin. Feed percentages are calculated as: (start_weight / bin_weight_after_fill) * 100, capped at 100%.

The bin_weight_after_fill represents the total bin weight immediately after feed was added, which accounts for any residual feed that was already in the bin. This is more accurate than using just the amount of feed added. For example, if a bin had 10kg residual and the farmer added 25kg in total (possibly across multiple rapid additions), bin_weight_after_fill would be 35kg, not 25kg.

Important: When calling detect_feed_additions(), multiple rapid additions to the same bin (within max_bin_time_gap) are automatically aggregated into a single event. The bin_weight_after_fill from that aggregation represents the bin weight after the final addition, which is exactly what this function needs for accurate percentage calculations.

Multi-day behavior: When processing a list of daily data, visits early in a day (before any feed addition on that day) are matched to feed additions from the previous calendar day. The function calculates the previous day by subtracting one day from the current date (extracted from the date column in visits or parsed from the list name). This works correctly even if:

• Days are provided out of chronological order in the list

• There are gaps in the data (missing days)

• Day names use different date formats

If the previous calendar day is not found in the data, or if dates cannot be parsed, visits before the first feed addition will have NA values. The first day in the list only uses its own feed additions.

Visits that occur before any feed addition to that bin (including previous day for multi-day lists) will have NA values.

Examples

# Create sample visit data
visits <- data.frame(
  date = "2024-01-01",
  cow = c("A", "A", "B", "B"),
  bin = c(1, 1, 2, 2),
  start = as.POSIXct(c(
    "2024-01-01 08:00:00",
    "2024-01-01 10:00:00",
    "2024-01-01 08:05:00",
    "2024-01-01 10:05:00"
  ), tz = "UTC"),
  end = as.POSIXct(c(
    "2024-01-01 08:10:00",
    "2024-01-01 10:10:00",
    "2024-01-01 08:15:00",
    "2024-01-01 10:15:00"
  ), tz = "UTC"),
  start_weight = c(50, 45, 50, 43),
  end_weight = c(45, 40, 43, 38)
)

# Detect per-bin feed additions
feed_additions <- detect_feed_additions(
  data = visits,
  aggregate_all_bin = FALSE
)

# Calculate feed availability at each visit
availability <- calculate_feed_availability(
  visit_data = visits,
  feed_addition_data = feed_additions
)

# Access visit-level data
visit_pct <- availability$visits

# Access daily summaries
daily_pct <- availability$daily_summary