We use two CGM example datasets shipped with iglu:
example_data_5_subject: 5 subjectsexample_data_hall: 19 subjectsdata(example_data_5_subject, package = "iglu")
data(example_data_hall, package = "iglu")
# Base-R summaries (no external dependencies)
summary_5 <- data.frame(
rows = nrow(example_data_5_subject),
subjects = length(unique(example_data_5_subject$id)),
time_min = min(example_data_5_subject$time),
time_max = max(example_data_5_subject$time),
gl_min = min(example_data_5_subject$gl, na.rm = TRUE),
gl_max = max(example_data_5_subject$gl, na.rm = TRUE)
)
summary_5
#> rows subjects time_min time_max gl_min gl_max
#> 1 13866 5 2015-02-24 17:31:29 2015-06-19 08:59:36 50 400iglu::episode_calculation() identifies
hypo/hyperglycemia episodes.
iglu_episodes_5 <- iglu::episode_calculation(
data = example_data_5_subject
)
print(iglu_episodes_5)
#> # A tibble: 35 × 7
#> id type level avg_ep_per_day avg_ep_duration avg_ep_gl total_episodes
#> <fct> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Subject 1 hypo lv1 0.0899 35 68.6 1
#> 2 Subject 1 hypo lv2 0 0 NA 0
#> 3 Subject 1 hypo exte… 0 0 NA 0
#> 4 Subject 1 hyper lv1 1.44 80.3 200. 16
#> 5 Subject 1 hyper lv2 0.180 30 264. 2
#> 6 Subject 1 hypo lv1_… 0.0899 35 68.6 1
#> 7 Subject 1 hyper lv1_… 1.26 79.6 195. 14
#> 8 Subject 2 hypo lv1 0 0 NA 0
#> 9 Subject 2 hypo lv2 0 0 NA 0
#> 10 Subject 2 hypo exte… 0 0 NA 0
#> # ℹ 25 more rowsiglu_episodes_hall <- iglu::episode_calculation(
data = example_data_hall
)
print(iglu_episodes_hall)
#> # A tibble: 133 × 7
#> id type level avg_ep_per_day avg_ep_duration avg_ep_gl total_episodes
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 1636-69-… hypo lv1 0.468 15 68.1 3
#> 2 1636-69-… hypo lv2 0 0 NA 0
#> 3 1636-69-… hypo exte… 0 0 NA 0
#> 4 1636-69-… hyper lv1 0.623 57.5 201. 4
#> 5 1636-69-… hyper lv2 0 0 NA 0
#> 6 1636-69-… hypo lv1_… 0.468 15 68.1 3
#> 7 1636-69-… hyper lv1_… 0.623 57.5 201. 4
#> 8 1636-69-… hypo lv1 0 0 NA 0
#> 9 1636-69-… hypo lv2 0 0 NA 0
#> 10 1636-69-… hypo exte… 0 0 NA 0
#> # ℹ 123 more rowsall_events_5 <- detect_all_events(example_data_5_subject, reading_minutes = 5)
print(all_events_5)
#> # 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: ¹avg_episode_duration_below_54all_events_hall <- detect_all_events(example_data_hall, reading_minutes = 5)
print(all_events_hall)
#> # A tibble: 152 × 6
#> id type level total_episodes avg_ep_per_day avg_episode_duration…¹
#> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 1636-69-001 hypo lv1 2 0 0
#> 2 1636-69-001 hypo lv2 0 0 0
#> 3 1636-69-001 hypo exten… 0 0 0
#> 4 1636-69-001 hypo lv1_e… 2 0 0
#> 5 1636-69-001 hyper lv1 4 0.01 0
#> 6 1636-69-001 hyper lv2 0 0 0
#> 7 1636-69-001 hyper exten… 0 0 0
#> 8 1636-69-001 hyper lv1_e… 4 0.01 0
#> 9 1636-69-026 hypo lv1 0 0 0
#> 10 1636-69-026 hypo lv2 0 0 0
#> # ℹ 142 more rows
#> # ℹ abbreviated name: ¹avg_episode_duration_below_54We compare performance using microbenchmark on both
datasets. Each benchmark contrasts
iglu::episode_calculation() with
cgmguru::detect_all_events().
library(microbenchmark)
library(iglu)
# example_data_5_subject
bench_5 <- microbenchmark(
episode_calculation = iglu::episode_calculation(example_data_5_subject),
detect_all_events = cgmguru::detect_all_events(example_data_5_subject, reading_minutes = 5),
times = 100,
unit = "ms"
)
print(bench_5)
#> Unit: milliseconds
#> expr min lq mean median uq
#> episode_calculation 376.474177 385.590486 390.380185 388.12088 392.58215
#> detect_all_events 1.682353 1.736063 2.226354 1.77243 1.79865
#> max neval cld
#> 432.52950 100 a
#> 47.41675 100 b
# example_data_hall (all subjects)
bench_hall <- microbenchmark(
episode_calculation = iglu::episode_calculation(example_data_hall),
detect_all_events = cgmguru::detect_all_events(example_data_hall, reading_minutes = 5),
times = 100,
unit = "ms"
)
print(bench_hall)
#> Unit: milliseconds
#> expr min lq mean median uq
#> episode_calculation 1078.02509 1099.925901 1126.088976 1110.985466 1130.421721
#> detect_all_events 3.86999 3.935713 4.080439 3.973228 4.035322
#> max neval cld
#> 1415.517087 100 a
#> 7.642236 100 b