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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 0These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.