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