R语言：RFID标签监测点停留时长计算的高效优化需求

Hey folks, I'm working with a dataset from an RFID location tracking system where I need to calculate how long each tag stays at a specific monitor point — specifically, the time interval before the MonitorID changes.

Dataset Snippet #

I can't generate randomized reproducible data, so here's a partial look at the records:

Time                 TagID    MonitorID  Location
2017-10-31 23:03:26  1427435  1352303    A4.18
2017-10-31 23:06:02  1427435  1352303    A4.18
……
2017-11-22 22:30:55  1427435  1349044    B6.24

Current Function & Performance Bottleneck #

I wrote the function below to compute elapsed time per monitor point, and it works as expected functionally. The problem? Processing a single monitor point takes ~4 minutes, and I need to run this for 95 points in batch — total runtime is way too slow for my needs.

Here's the current code:

elapsed_time <- function(x) {
  # Prepare variables
  current_monitor <- x$MonitorID[1]
  start_time <- x$Time[1]
  end_time <- NULL
  output <- data.frame("Date" = as.POSIXct(as.character()), "MonitorID" = as.integer(), "Minutes_elapsed" = as.integer())
  # For loop to iterate over rows
  for (i in 1:nrow(x)) {
    # Skip if monitor hasn't changed (and not last row)
    if (x$MonitorID[i] == current_monitor & i != nrow(x)) {
      next
    } else {
      # Mark end time when location changes
      end_time <- x$Time[i]
      # Calculate time difference in minutes
      time_spent <- difftime(end_time, start_time, units = "mins")
      # Create temp row to append
      temp <- data.frame(start_time, current_monitor, time_spent)
      # Append to output
      output <- rbind(output, setNames(temp, names(output)))
      # Reset start time and current monitor for next segment
      start_time <- end_time
      current_monitor <- x$MonitorID[i]
    }
  }
  # Join with monitor-to-location mappings (can ignore this line for optimization focus)
  output <- left_join(output, Mmappings[,c(1,2)], by="MonitorID")
  return(output)
}

Example Output #

Date                 MonitorID Minutes_elapsed  Location
1 2017-10-31 23:03:26 1352303    3.36666667 mins  A4.18
……
6 2017-10-31 23:07:47 0         0.05000000 mins   A4.20

(Note: Short intervals between monitor points are due to reading fluctuations, which doesn't impact my core requirements.)

What I'm Looking For #

I need practical, efficient optimization strategies to speed up this function, especially for batch processing 95 monitor points. Any ideas to replace the slow loop, improve data handling, or leverage vectorized operations would be a huge help!

内容的提问来源于stack exchange，提问作者anthillcode