# detect_hypoglycemic_events function

# detect_hypoglycemic_events

Runs the documented examples for `detect_hypoglycemic_events()`.

    example(detect_hypoglycemic_events, package = "cgmguru", run.dontrun = FALSE)
    #>
    #> dtct__> # Load sample data
    #> dtct__> library(iglu)
    #>
    #> dtct__> data(example_data_5_subject)
    #>
    #> dtct__> data(example_data_hall)
    #>
    #> dtct__> # Level 1 Hypoglycemia Event (>=15 consecutive min of <70 mg/dL and event
    #> dtct__> # ends when there is >=15 consecutive min with a CGM sensor value of >=70 mg/dL)
    #> dtct__> hypo_lv1 <- detect_hypoglycemic_events(example_data_5_subject, type = "lv1")
    #>
    #> dtct__> print(hypo_lv1$events_total)
    #> # A tibble: 5 × 3
    #>   id        total_episodes avg_ep_per_day
    #>   <chr>              <int>          <dbl>
    #> 1 Subject 1              1           0.09
    #> 2 Subject 2              0           0
    #> 3 Subject 3              1           0.18
    #> 4 Subject 4              2           0.16
    #> 5 Subject 5              1           0.1
    #>
    #> dtct__> # Level 2 Hypoglycemia Event (>=15 consecutive min of <54 mg/dL and event
    #> dtct__> # ends when there is >=15 consecutive min with a CGM sensor value of >=54 mg/dL)
    #> dtct__> hypo_lv2 <- detect_hypoglycemic_events(example_data_5_subject, type = "lv2")
    #>
    #> dtct__> # Extended Hypoglycemia Event (>120 consecutive min of <70 mg/dL and event
    #> dtct__> # ends when there is >=15 consecutive min with a CGM sensor value of >=70 mg/dL)
    #> dtct__> hypo_extended <- detect_hypoglycemic_events(example_data_5_subject, type = "extended")
    #>
    #> dtct__> print(hypo_extended$events_total)
    #> # A tibble: 5 × 3
    #>   id        total_episodes 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
    #>
    #> dtct__> # Custom criteria method for the same standard definitions
    #> dtct__> hypo_lv1_custom <- detect_hypoglycemic_events(
    #> dtct__+   example_data_5_subject,
    #> dtct__+   start_gl = 70,
    #> dtct__+   dur_length = 15,
    #> dtct__+   end_length = 15
    #> dtct__+ )
    #>
    #> dtct__> hypo_lv2_custom <- detect_hypoglycemic_events(
    #> dtct__+   example_data_5_subject,
    #> dtct__+   start_gl = 54,
    #> dtct__+   dur_length = 15,
    #> dtct__+   end_length = 15
    #> dtct__+ )
    #>
    #> dtct__> hypo_extended_custom <- detect_hypoglycemic_events(
    #> dtct__+   example_data_5_subject,
    #> dtct__+   start_gl = 70,
    #> dtct__+   dur_length = 120,
    #> dtct__+   end_length = 15
    #> dtct__+ )
    #>
    #> dtct__> # Compare event rates across levels
    #> dtct__> cat("Level 1 episodes:", sum(hypo_lv1$events_total$total_episodes), "\n")
    #> Level 1 episodes: 5
    #>
    #> dtct__> cat("Level 2 episodes:", sum(hypo_lv2$events_total$total_episodes), "\n")
    #> Level 2 episodes: 0
    #>
    #> dtct__> cat("Extended episodes:", sum(hypo_extended$events_total$total_episodes), "\n")
    #> Extended episodes: 0
    #>
    #> dtct__> # Analysis on larger dataset with Level 1 criteria
    #> dtct__> large_hypo <- detect_hypoglycemic_events(example_data_hall, type = "lv1")
    #>
    #> dtct__> print(large_hypo$events_total)
    #> # A tibble: 19 × 3
    #>    id           total_episodes avg_ep_per_day
    #>    <chr>                 <int>          <dbl>
    #>  1 1636-69-001               3           0.47
    #>  2 1636-69-026               0           0
    #>  3 1636-69-032               0           0
    #>  4 1636-69-090               4           0.61
    #>  5 1636-69-091               0           0
    #>  6 1636-69-114               0           0
    #>  7 1636-70-1005              2           0.31
    #>  8 1636-70-1010              5           0.78
    #>  9 2133-004                  2           0.32
    #> 10 2133-015                  2           0.31
    #> 11 2133-017                  0           0
    #> 12 2133-018                  0           0
    #> 13 2133-019                  3           0.47
    #> 14 2133-021                  1           0.16
    #> 15 2133-024                  8           1.26
    #> 16 2133-027                  3           0.44
    #> 17 2133-035                  1           0.15
    #> 18 2133-036                  8           1.1
    #> 19 2133-039                 10           1.33
    #>
    #> dtct__> # Analysis on larger dataset with Level 2 criteria
    #> dtct__> large_hypo_lv2 <- detect_hypoglycemic_events(example_data_hall, type = "lv2")
    #>
    #> dtct__> print(large_hypo_lv2$events_total)
    #> # A tibble: 19 × 3
    #>    id           total_episodes avg_ep_per_day
    #>    <chr>                 <int>          <dbl>
    #>  1 1636-69-001               0           0
    #>  2 1636-69-026               0           0
    #>  3 1636-69-032               0           0
    #>  4 1636-69-090               0           0
    #>  5 1636-69-091               0           0
    #>  6 1636-69-114               0           0
    #>  7 1636-70-1005              1           0.15
    #>  8 1636-70-1010              0           0
    #>  9 2133-004                  0           0
    #> 10 2133-015                  0           0
    #> 11 2133-017                  0           0
    #> 12 2133-018                  0           0
    #> 13 2133-019                  0           0
    #> 14 2133-021                  0           0
    #> 15 2133-024                  1           0.16
    #> 16 2133-027                  0           0
    #> 17 2133-035                  0           0
    #> 18 2133-036                  0           0
    #> 19 2133-039                  1           0.13
    #>
    #> dtct__> # Analysis on larger dataset with Extended criteria
    #> dtct__> large_hypo_extended <- detect_hypoglycemic_events(example_data_hall, type = "extended")
    #>
    #> dtct__> print(large_hypo_extended$events_total)
    #> # A tibble: 19 × 3
    #>    id           total_episodes avg_ep_per_day
    #>    <chr>                 <int>          <dbl>
    #>  1 1636-69-001               0           0
    #>  2 1636-69-026               0           0
    #>  3 1636-69-032               0           0
    #>  4 1636-69-090               0           0
    #>  5 1636-69-091               0           0
    #>  6 1636-69-114               0           0
    #>  7 1636-70-1005              0           0
    #>  8 1636-70-1010              1           0.16
    #>  9 2133-004                  0           0
    #> 10 2133-015                  0           0
    #> 11 2133-017                  0           0
    #> 12 2133-018                  0           0
    #> 13 2133-019                  0           0
    #> 14 2133-021                  0           0
    #> 15 2133-024                  1           0.16
    #> 16 2133-027                  1           0.15
    #> 17 2133-035                  0           0
    #> 18 2133-036                  1           0.14
    #> 19 2133-039                  0           0