# cgmguru: Complete CGM Analysis Workflow

The `cgmguru` package provides comprehensive tools for analyzing Continuous Glucose Monitoring (CGM) data using the GRID (Glucose Rate Increase Detector) algorithm and related methodologies. This vignette demonstrates the complete workflow from basic analysis to advanced event detection.

## Core functions at a glance

- grid: Implements GRID to detect rapid glucose rate increases (meal starts); returns grid vector and episode summaries.
- mod_grid: Re-applies GRID logic with a defined gap and window to produce a modified grid for maxima mapping.
- maxima_grid: Combines GRID and local maxima to identify postprandial peaks within a time window and summarizes episodes.
- detect_hyperglycemic_events: Finds Level 1/Level 2/Extended hyperglycemia episodes using consensus thresholds and durations.
- detect_hypoglycemic_events: Finds Level 1/Level 2/Extended hypoglycemia episodes and duration below 54 mg/dL where applicable.
- detect_all_events: Unified interface summarizing hypo/hyper event types, counts, and durations by subject.
- find_local_maxima: Detects peaks where glucose rises for two points before and falls for two points after the peak.
- find_max_after_hours / find_max_before_hours: Finds maximum glucose within a time window after/before given start points; returns indices and episode counts.
- find_min_after_hours / find_min_before_hours: Finds minimum glucose within a time window after/before given start points.
- find_new_maxima: Refines maxima around candidate grid points to improve peak identification.
- detect_between_maxima: Characterizes events occurring between identified maxima, including time-to-peak.
- excursion: Calculates excursions (>70 mg/dL rise within 2 hours, not preceded by <70 mg/dL), with counts and starts.
- start_finder: Locates R-based positions of episode starts in a 0/1 vector (1 after 0 or at start).
- transform_df: Maps GRID episode starts to maxima (within 4 hours) and merges results for downstream analysis.
- orderfast: Efficiently orders data by `id` and `time` for large CGM datasets.

Tip: See individual help pages for details and examples, for instance:

    ?grid
    ?detect_all_events

## Loading Sample Data

We’ll use two datasets from the `iglu` package to demonstrate different analysis scenarios:

    # Load example datasets
    data(example_data_5_subject)  # 5 subjects, 13,866 readings
    data(example_data_hall)       # 19 subjects, 34,890 readings

    # Display basic information about the datasets
    cat("Dataset 1 (example_data_5_subject):\n")
    #> Dataset 1 (example_data_5_subject):
    cat("  Rows:", nrow(example_data_5_subject), "\n")
    #>   Rows: 13866
    cat("  Subjects:", length(unique(example_data_5_subject$id)), "\n")
    #>   Subjects: 5
    cat("  Time range:", as.character(range(example_data_5_subject$time)), "\n")
    #>   Time range: 2015-02-24 17:31:29 2015-06-19 08:59:36
    cat("  Glucose range:", range(example_data_5_subject$gl), "mg/dL\n\n")
    #>   Glucose range: 50 400 mg/dL

    cat("Dataset 2 (example_data_hall):\n")
    #> Dataset 2 (example_data_hall):
    cat("  Rows:", nrow(example_data_hall), "\n")
    #>   Rows: 34890
    cat("  Subjects:", length(unique(example_data_hall$id)), "\n")
    #>   Subjects: 19
    cat("  Time range:", as.character(range(example_data_hall$time)), "\n")
    #>   Time range: 2014-02-03 03:42:12 2017-06-14 13:57:42
    cat("  Glucose range:", range(example_data_hall$gl), "mg/dL\n")
    #>   Glucose range: 41 303 mg/dL

    # Show first few rows
    head(example_data_5_subject)
    #>          id                time  gl
    #> 1 Subject 1 2015-06-06 16:50:27 153
    #> 2 Subject 1 2015-06-06 17:05:27 137
    #> 3 Subject 1 2015-06-06 17:10:27 128
    #> 4 Subject 1 2015-06-06 17:15:28 121
    #> 5 Subject 1 2015-06-06 17:25:27 120
    #> 6 Subject 1 2015-06-06 17:45:27 138

## 1. Basic GRID Analysis

The GRID algorithm detects rapid glucose rate increases, which are often associated with meal consumption.

    # Perform GRID analysis on the smaller dataset
    grid_result <- grid(example_data_5_subject, gap = 15, threshold = 130)

    # Display results
    cat("GRID Analysis Results:\n")
    #> GRID Analysis Results:
    cat("  Detected grid points:", nrow(grid_result$grid_vector), "\n")
    #>   Detected grid points: 13866
    cat("  Episode counts:\n")
    #>   Episode counts:
    print(grid_result$episode_counts)
    #> # A tibble: 5 × 2
    #>   id        episode_counts
    #>   <chr>              <int>
    #> 1 Subject 1             10
    #> 2 Subject 2             22
    #> 3 Subject 3              7
    #> 4 Subject 4             18
    #> 5 Subject 5             42

    # Show first few detected grid points
    cat("\nFirst few detected grid points:\n")
    #>
    #> First few detected grid points:
    head(grid_result$grid_vector)
    #> # A tibble: 6 × 1
    #>    grid
    #>   <int>
    #> 1     0
    #> 2     0
    #> 3     0
    #> 4     0
    #> 5     0
    #> 6     0

## 2. Hyperglycemic Event Detection

Detect different levels of hyperglycemic events according to clinical guidelines.

    # Level 1 Hyperglycemic events (≥15 consecutive minutes >180 mg/dL)
    hyper_lv1 <- detect_hyperglycemic_events(
      example_data_5_subject,
      start_gl = 180,
      dur_length = 15,
      end_length = 15,
      end_gl = 180
    )

    # Level 2 Hyperglycemic events (≥15 consecutive minutes >250 mg/dL)
    hyper_lv2 <- detect_hyperglycemic_events(
      example_data_5_subject,
      start_gl = 250,
      dur_length = 15,
      end_length = 15,
      end_gl = 250
    )

    # Extended Hyperglycemic events (default parameters)
    hyper_extended <- detect_hyperglycemic_events(example_data_5_subject)

    cat("Hyperglycemic Event Detection Results:\n")
    #> Hyperglycemic Event Detection Results:
    cat("Level 1 Events (>180 mg/dL):\n")
    #> Level 1 Events (>180 mg/dL):
    print(hyper_lv1$events_total)
    #> # A tibble: 5 × 3
    #>   id        total_events avg_ep_per_day
    #>   <chr>            <int>          <dbl>
    #> 1 Subject 1           14           1.1
    #> 2 Subject 2           17           1.02
    #> 3 Subject 3            8           1.39
    #> 4 Subject 4           13           1.01
    #> 5 Subject 5           34           3.21

    cat("\nLevel 2 Events (>250 mg/dL):\n")
    #>
    #> Level 2 Events (>250 mg/dL):
    print(hyper_lv2$events_total)
    #> # A tibble: 5 × 3
    #>   id        total_events avg_ep_per_day
    #>   <chr>            <int>          <dbl>
    #> 1 Subject 1            2           0.16
    #> 2 Subject 2           18           1.08
    #> 3 Subject 3            4           0.69
    #> 4 Subject 4            0           0
    #> 5 Subject 5           17           1.6

    cat("\nExtended Events (default):\n")
    #>
    #> Extended Events (default):
    print(hyper_extended$events_total)
    #> # A tibble: 5 × 3
    #>   id        total_events avg_ep_per_day
    #>   <chr>            <int>          <dbl>
    #> 1 Subject 1            0           0
    #> 2 Subject 2            9           0.54
    #> 3 Subject 3            2           0.35
    #> 4 Subject 4            0           0
    #> 5 Subject 5           10           0.94

    # Show detailed events for first subject
    cat("\nDetailed Level 1 Events for Subject", hyper_lv1$events_detailed$id[1], ":\n")
    #>
    #> Detailed Level 1 Events for Subject Subject 1 :
    head(hyper_lv1$events_detailed[hyper_lv1$events_detailed$id == hyper_lv1$events_detailed$id[1], ])
    #> # A tibble: 6 × 7
    #>   id        start_time          start_glucose end_time            end_glucose
    #>   <chr>     <dttm>                      <dbl> <dttm>                    <dbl>
    #> 1 Subject 1 2015-06-11 15:45:07           194 2015-06-11 17:10:07         157
    #> 2 Subject 1 2015-06-11 17:25:07           195 2015-06-11 20:05:06         142
    #> 3 Subject 1 2015-06-11 22:35:06           187 2015-06-12 00:05:06         151
    #> 4 Subject 1 2015-06-12 07:50:04           181 2015-06-12 09:35:04         164
    #> 5 Subject 1 2015-06-13 17:04:59           181 2015-06-13 18:40:00         155
    #> 6 Subject 1 2015-06-13 19:44:59           223 2015-06-13 20:49:58         158
    #> # ℹ 2 more variables: start_indices <int>, end_indices <int>

## 3. Hypoglycemic Event Detection

Detect hypoglycemic events using different thresholds.

    # Level 1 Hypoglycemic events (≤70 mg/dL)
    hypo_lv1 <- detect_hypoglycemic_events(
      example_data_5_subject,
      start_gl = 70,
      dur_length = 15,
      end_length = 15
    )

    # Level 2 Hypoglycemic events (≤54 mg/dL)
    hypo_lv2 <- detect_hypoglycemic_events(
      example_data_5_subject,
      start_gl = 54,
      dur_length = 15,
      end_length = 15
    )

    cat("Hypoglycemic Event Detection Results:\n")
    #> Hypoglycemic Event Detection Results:
    cat("Level 1 Events (≤70 mg/dL):\n")
    #> Level 1 Events (≤70 mg/dL):
    print(hypo_lv1$events_total)
    #> # A tibble: 5 × 3
    #>   id        total_events avg_ep_per_day
    #>   <chr>            <int>          <dbl>
    #> 1 Subject 1            1           0.08
    #> 2 Subject 2            0           0
    #> 3 Subject 3            1           0.17
    #> 4 Subject 4            2           0.16
    #> 5 Subject 5            1           0.09

    cat("\nLevel 2 Events (≤54 mg/dL):\n")
    #>
    #> Level 2 Events (≤54 mg/dL):
    print(hypo_lv2$events_total)
    #> # A tibble: 5 × 3
    #>   id        total_events avg_ep_per_day
    #>   <chr>            <int>          <dbl>
    #> 1 Subject 1            0              0
    #> 2 Subject 2            0              0
    #> 3 Subject 3            0              0
    #> 4 Subject 4            0              0
    #> 5 Subject 5            0              0

## 4. Comprehensive Event Detection

Detect all types of glycemic events in one analysis.

    # Detect all events with 5-minute reading intervals
    all_events <- detect_all_events(example_data_5_subject, reading_minutes = 5)

    cat("Comprehensive Event Detection Results:\n")
    #> Comprehensive Event Detection Results:
    print(all_events)
    #> # A tibble: 40 × 6
    #>    id        type  level    total_episodes avg_ep_per_day avg_episode_duration…¹
    #>    <chr>     <chr> <chr>             <int>          <dbl>                  <dbl>
    #>  1 Subject 1 hypo  lv1                   1           0.08                      0
    #>  2 Subject 1 hypo  lv2                   0           0                         0
    #>  3 Subject 1 hypo  extended              0           0                         0
    #>  4 Subject 1 hypo  lv1_excl              1           0.08                      0
    #>  5 Subject 1 hyper lv1                  14           1.1                       0
    #>  6 Subject 1 hyper lv2                   2           0.16                      0
    #>  7 Subject 1 hyper extended              0           0                         0
    #>  8 Subject 1 hyper lv1_excl             12           0.95                      0
    #>  9 Subject 2 hypo  lv1                   0           0                         0
    #> 10 Subject 2 hypo  lv2                   0           0                         0
    #> # ℹ 30 more rows
    #> # ℹ abbreviated name: ¹​avg_episode_duration_below_54

## 5. Local Maxima Detection

Identify local maxima in glucose time series, which are important for postprandial peak analysis.

    # Find local maxima
    maxima_result <- find_local_maxima(example_data_5_subject)

    cat("Local Maxima Detection Results:\n")
    #> Local Maxima Detection Results:
    cat("  Total local maxima found:", nrow(maxima_result$local_maxima_vector), "\n")
    #>   Total local maxima found: 1602
    cat("  Merged results:", nrow(maxima_result$merged_results), "\n")
    #>   Merged results: 1602

    # Show first few maxima
    head(maxima_result$local_maxima_vector)
    #> # A tibble: 6 × 1
    #>   local_maxima
    #>          <int>
    #> 1            8
    #> 2           23
    #> 3           24
    #> 4           65
    #> 5           70
    #> 6           77

## 6. Maxima-GRID Combined Analysis

Combine maxima detection with GRID analysis for comprehensive postprandial peak detection.

    # Combined maxima and GRID analysis
    maxima_grid_result <- maxima_grid(
      example_data_5_subject,
      threshold = 130,
      gap = 60,
      hours = 2
    )

    cat("Maxima-GRID Combined Analysis Results:\n")
    #> Maxima-GRID Combined Analysis Results:
    cat("  Detected maxima:", nrow(maxima_grid_result$results), "\n")
    #>   Detected maxima: 88
    cat("  Episode counts:\n")
    #>   Episode counts:
    print(maxima_grid_result$episode_counts)
    #> # A tibble: 5 × 2
    #>   id        episode_counts
    #>   <chr>              <int>
    #> 1 Subject 1              8
    #> 2 Subject 2             18
    #> 3 Subject 3              7
    #> 4 Subject 4             16
    #> 5 Subject 5             39

    # Show first few results
    head(maxima_grid_result$results)
    #> # A tibble: 6 × 8
    #>   id    grid_time grid_gl maxima_time maxima_glucose time_to_peak_min grid_index
    #>   <chr> <dttm>      <dbl> <dttm>               <dbl>            <dbl>      <int>
    #> 1 Subj… 2015-06-…     143 2015-06-11…            276               40        967
    #> 2 Subj… 2015-06-…     135 2015-06-11…            209               50       1039
    #> 3 Subj… 2015-06-…     160 2015-06-12…            210               40       1155
    #> 4 Subj… 2015-06-…     132 2015-06-13…            202               60       1416
    #> 5 Subj… 2015-06-…     176 2015-06-14…            227               45       1677
    #> 6 Subj… 2015-06-…     166 2015-06-16…            208               65       2223
    #> # ℹ 1 more variable: maxima_index <int>

## 7. Excursion Analysis

Analyze glucose excursions above a threshold.

    # Excursion analysis
    excursion_result <- excursion(example_data_5_subject, gap = 15)

    cat("Excursion Analysis Results:\n")
    #> Excursion Analysis Results:
    cat("  Excursion vector length:", length(excursion_result$excursion_vector), "\n")
    #>   Excursion vector length: 1
    cat("  Episode counts:\n")
    #>   Episode counts:
    print(excursion_result$episode_counts)
    #> # A tibble: 5 × 2
    #>   id        episode_counts
    #>   <chr>              <int>
    #> 1 Subject 1              9
    #> 2 Subject 2             14
    #> 3 Subject 3             11
    #> 4 Subject 4             17
    #> 5 Subject 5             34

    # Show episode start information
    head(excursion_result$episode_start)
    #> # A tibble: 6 × 4
    #>   id        time                   gl indices
    #>   <chr>     <dttm>              <dbl>   <int>
    #> 1 Subject 1 2015-06-11 13:40:07    87     948
    #> 2 Subject 1 2015-06-11 16:50:07   187     981
    #> 3 Subject 1 2015-06-11 20:40:06   120    1026
    #> 4 Subject 1 2015-06-13 14:49:59    95    1399
    #> 5 Subject 1 2015-06-14 14:39:55   113    1649
    #> 6 Subject 1 2015-06-15 13:49:52    85    1895

## 8. Advanced Pipeline: Complete Workflow

Demonstrate the complete analysis pipeline using the larger dataset for more comprehensive results. Note: This section may take longer to run on some machines.

    # Use the larger dataset for comprehensive analysis
    cat("Running complete analysis pipeline on example_data_hall...\n")
    #> Running complete analysis pipeline on example_data_hall...

    # Step 1: GRID analysis
    cat("Step 1: GRID Analysis\n")
    #> Step 1: GRID Analysis
    grid_pipeline <- grid(example_data_hall, gap = 15, threshold = 130)
    cat("  Detected", nrow(grid_pipeline$grid_vector), "grid points\n")
    #>   Detected 34890 grid points

    # Step 2: Local maxima detection
    cat("Step 2: Local Maxima Detection\n")
    #> Step 2: Local Maxima Detection
    maxima_pipeline <- find_local_maxima(example_data_hall)
    cat("  Found", nrow(maxima_pipeline$local_maxima_vector), "local maxima\n")
    #>   Found 4991 local maxima

    # Step 3: Modified GRID analysis
    cat("Step 3: Modified GRID Analysis\n")
    #> Step 3: Modified GRID Analysis
    mod_grid_pipeline <- mod_grid(
      example_data_hall,
      grid_pipeline$grid_vector,
      hours = 2,
      gap = 15
    )
    cat("  Modified grid points:", nrow(mod_grid_pipeline$mod_grid_vector), "\n")
    #>   Modified grid points: 34890

    # Step 4: Find maximum points after modified GRID points
    cat("Step 4: Finding Maximum Points After GRID Points\n")
    #> Step 4: Finding Maximum Points After GRID Points
    max_after_pipeline <- find_max_after_hours(
      example_data_hall,
      mod_grid_pipeline$mod_grid_vector,
      hours = 2
    )
    cat("  Maximum points found:", length(max_after_pipeline$max_indices), "\n")
    #>   Maximum points found: 1

    # Step 5: Find new maxima
    cat("Step 5: Finding New Maxima\n")
    #> Step 5: Finding New Maxima
    new_maxima_pipeline <- find_new_maxima(
      example_data_hall,
      max_after_pipeline$max_indices,
      maxima_pipeline$local_maxima_vector
    )
    cat("  New maxima identified:", nrow(new_maxima_pipeline), "\n")
    #>   New maxima identified: 2

    # Step 6: Transform dataframes
    cat("Step 6: Transforming Dataframes\n")
    #> Step 6: Transforming Dataframes
    transformed_pipeline <- transform_df(
      grid_pipeline$episode_start,
      new_maxima_pipeline
    )
    cat("  Transformed dataframe rows:", nrow(transformed_pipeline), "\n")
    #>   Transformed dataframe rows: 0

    # Step 7: Detect between maxima
    cat("Step 7: Detecting Between Maxima\n")
    #> Step 7: Detecting Between Maxima
    between_maxima_pipeline <- detect_between_maxima(
      example_data_hall,
      transformed_pipeline
    )
    cat("  Between maxima analysis completed\n")
    #>   Between maxima analysis completed

    cat("\nComplete pipeline executed successfully!\n")
    #>
    #> Complete pipeline executed successfully!

## 9. Time-Based Analysis Functions

Demonstrate functions that find maximum and minimum values within specific time windows.

    # Create a subset for demonstration
    subset_data <- example_data_5_subject[example_data_5_subject$id == unique(example_data_5_subject$id)[1], ][1:100, ]

    # Create start points for time-based analysis
    start_points <- subset_data[seq(1, nrow(subset_data), by = 20), ]

    cat("Time-Based Analysis Functions:\n")
    #> Time-Based Analysis Functions:

    # Find maximum after 1 hour
    max_after <- find_max_after_hours(subset_data, start_points, hours = 1)
    cat("  Max after 1 hour:", length(max_after$max_indices), "points\n")
    #>   Max after 1 hour: 1 points

    # Find maximum before 1 hour
    max_before <- find_max_before_hours(subset_data, start_points, hours = 1)
    cat("  Max before 1 hour:", length(max_before$max_indices), "points\n")
    #>   Max before 1 hour: 1 points

    # Find minimum after 1 hour
    min_after <- find_min_after_hours(subset_data, start_points, hours = 1)
    cat("  Min after 1 hour:", length(min_after$min_indices), "points\n")
    #>   Min after 1 hour: 1 points

    # Find minimum before 1 hour
    min_before <- find_min_before_hours(subset_data, start_points, hours = 1)
    cat("  Min before 1 hour:", length(min_before$min_indices), "points\n")
    #>   Min before 1 hour: 1 points

## 10. Data Ordering Utility

Demonstrate the fast dataframe ordering utility.

    # Create sample data with mixed order
    sample_data <- data.frame(
      id = c("b", "a", "c", "a", "b"),
      time = as.POSIXct(c("2023-01-01 10:00:00", "2023-01-01 09:00:00",
                          "2023-01-01 11:00:00", "2023-01-01 08:00:00",
                          "2023-01-01 12:00:00"), tz = "UTC"),
      gl = c(120, 100, 140, 90, 130)
    )

    cat("Original data (unordered):\n")
    #> Original data (unordered):
    print(sample_data)
    #>   id                time  gl
    #> 1  b 2023-01-01 10:00:00 120
    #> 2  a 2023-01-01 09:00:00 100
    #> 3  c 2023-01-01 11:00:00 140
    #> 4  a 2023-01-01 08:00:00  90
    #> 5  b 2023-01-01 12:00:00 130

    # Order the data
    ordered_data <- orderfast(sample_data)

    cat("\nOrdered data:\n")
    #>
    #> Ordered data:
    print(ordered_data)
    #>   id                time  gl
    #> 4  a 2023-01-01 08:00:00  90
    #> 2  a 2023-01-01 09:00:00 100
    #> 1  b 2023-01-01 10:00:00 120
    #> 5  b 2023-01-01 12:00:00 130
    #> 3  c 2023-01-01 11:00:00 140

## 11. Visualization Examples

Create visualizations to better understand the analysis results.

    # Select one subject for visualization
    subject_id <- unique(example_data_5_subject$id)[1]
    subject_data <- example_data_5_subject[example_data_5_subject$id == subject_id, ]

    # Create a comprehensive plot
    p1 <- ggplot(subject_data, aes(x = time, y = gl)) +
      geom_line(color = "blue", alpha = 0.7, size = 0.5) +
      geom_hline(yintercept = 180, color = "red", linetype = "dashed", alpha = 0.8) +
      geom_hline(yintercept = 250, color = "darkred", linetype = "dashed", alpha = 0.8) +
      geom_hline(yintercept = 70, color = "orange", linetype = "dashed", alpha = 0.8) +
      geom_hline(yintercept = 54, color = "darkorange", linetype = "dashed", alpha = 0.8) +
      labs(title = paste("CGM Data for Subject", subject_id, "with Clinical Thresholds"),
           subtitle = "Red lines: Hyperglycemia thresholds (180, 250 mg/dL)\nOrange lines: Hypoglycemia thresholds (70, 54 mg/dL)",
           x = "Time",
           y = "Glucose (mg/dL)") +
      theme_minimal() +
      theme(plot.title = element_text(size = 14, face = "bold"),
            plot.subtitle = element_text(size = 10))
    #> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
    #> ℹ Please use `linewidth` instead.
    #> This warning is displayed once every 8 hours.
    #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
    #> generated.

    print(p1)

    # Create a summary plot showing event counts across subjects
    event_summary <- hyper_lv1$events_total
    event_summary$subject <- paste("Subject", event_summary$id)

    p2 <- ggplot(event_summary, aes(x = subject, y = total_events)) +
      geom_col(fill = "steelblue", alpha = 0.7) +
      geom_text(aes(label = total_events), vjust = -0.5) +
      labs(title = "Level 1 Hyperglycemic Events by Subject",
           x = "Subject",
           y = "Number of Events") +
      theme_minimal() +
      theme(axis.text.x = element_text(angle = 45, hjust = 1))

    print(p2)

## 12. Performance Comparison

Compare performance between datasets of different sizes.

    # Function to measure execution time
    measure_time <- function(expr) {
      start_time <- Sys.time()
      result <- eval(expr)
      end_time <- Sys.time()
      return(list(result = result, time = as.numeric(end_time - start_time, units = "secs")))
    }

    cat("Performance Comparison:\n")
    #> Performance Comparison:

    # Test on smaller dataset
    cat("Small dataset (5 subjects, 13,866 readings):\n")
    #> Small dataset (5 subjects, 13,866 readings):
    small_time <- measure_time(grid(example_data_5_subject, gap = 15, threshold = 130))
    cat("  GRID analysis time:", round(small_time$time, 3), "seconds\n")
    #>   GRID analysis time: 0.001 seconds

    small_maxima_time <- measure_time(find_local_maxima(example_data_5_subject))
    cat("  Local maxima time:", round(small_maxima_time$time, 3), "seconds\n")
    #>   Local maxima time: 0.001 seconds

    # Test on larger dataset
    cat("\nLarge dataset (19 subjects, 34,890 readings):\n")
    #>
    #> Large dataset (19 subjects, 34,890 readings):
    large_time <- measure_time(grid(example_data_hall, gap = 15, threshold = 130))
    cat("  GRID analysis time:", round(large_time$time, 3), "seconds\n")
    #>   GRID analysis time: 0.002 seconds

    large_maxima_time <- measure_time(find_local_maxima(example_data_hall))
    cat("  Local maxima time:", round(large_maxima_time$time, 3), "seconds\n")
    #>   Local maxima time: 0.003 seconds

    # Calculate efficiency
    efficiency_ratio <- (large_time$time / large_time$result$episode_counts$total_episodes) /
                        (small_time$time / small_time$result$episode_counts$total_episodes)
    #> Warning: Unknown or uninitialised column: `total_episodes`.
    #> Unknown or uninitialised column: `total_episodes`.
    cat("\nEfficiency ratio (large/small):", round(efficiency_ratio, 2))
    #>
    #> Efficiency ratio (large/small):

## Summary

This vignette demonstrates the comprehensive capabilities of the `cgmguru` package:

### Core Analysis Functions:

- GRID Analysis: Detects rapid glucose rate increases
- Event Detection: Identifies hyperglycemic and hypoglycemic events
- Local Maxima: Finds peaks in glucose time series
- Excursion Analysis: Analyzes glucose excursions above thresholds

### Advanced Pipeline Functions:

- Maxima-GRID Combination: Integrated analysis for postprandial peaks
- Time-Based Analysis: Finds maximum/minimum values within time windows
- Complete Workflow: End-to-end analysis pipeline
- Data Transformation: Prepares data for downstream analysis

### Utility Functions:

- Fast Ordering: Efficient dataframe sorting
- Input Validation: Robust error handling and parameter validation

### Key Features:

- High Performance: C++ implementation for speed
- Clinical Relevance: Based on established CGM analysis methodologies
- Comprehensive Coverage: Handles various glycemic event types
- Flexible Parameters: Customizable thresholds and time windows
- Robust Validation: Input validation and error handling

The package is designed for both research and clinical applications, providing reliable and efficient tools for CGM data analysis. For more detailed function documentation, see `help(package = "cgmguru")`.

## References

- Battelino, T., et al. “Continuous glucose monitoring and metrics for clinical trials: an international consensus statement.” The Lancet Diabetes & Endocrinology 11.1 (2023): 42-57.
- Harvey, Rebecca A., et al. “Design of the glucose rate increase detector: a meal detection module for the health monitoring system.” Journal of diabetes science and technology 8.2 (2014): 307-320.
- Chun, E., et al. “iglu: interpreting glucose data from continuous glucose monitors.” R package version 3.0 (2023).