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Prepares data for downstream analysis by mapping GRID episode starts to maxima within a 4-hour window and merging the information.
grid() (typically
episode_start)find_new_maxima() or
local maxima resultsid, grid_time,
grid_gl, maxima_time,
maxima_glexample(transform_df, package = "cgmguru", run.dontrun = FALSE)
#>
#> trnsf_> # Load sample data
#> trnsf_> library(iglu)
#>
#> trnsf_> data(example_data_5_subject)
#>
#> trnsf_> data(example_data_hall)
#>
#> trnsf_> # Complete pipeline example with smaller dataset
#> trnsf_> threshold <- 130
#>
#> trnsf_> gap <- 60
#>
#> trnsf_> hours <- 2
#>
#> trnsf_> # 1) Find GRID points
#> trnsf_> grid_result <- grid(example_data_5_subject, gap = gap, threshold = threshold)
#>
#> trnsf_> # 2) Find modified GRID points before 2 hours minimum
#> trnsf_> mod_grid <- mod_grid(example_data_5_subject,
#> trnsf_+ start_finder(grid_result$grid_vector),
#> trnsf_+ hours = hours,
#> trnsf_+ gap = gap)
#>
#> trnsf_> # 3) Find maximum point 2 hours after mod_grid point
#> trnsf_> mod_grid_maxima <- find_max_after_hours(example_data_5_subject,
#> trnsf_+ start_finder(mod_grid$mod_grid_vector),
#> trnsf_+ hours = hours)
#>
#> trnsf_> # 4) Identify local maxima around episodes/windows
#> trnsf_> local_maxima <- find_local_maxima(example_data_5_subject)
#>
#> trnsf_> # 5) Among local maxima, find maximum point after two hours
#> trnsf_> final_maxima <- find_new_maxima(example_data_5_subject,
#> trnsf_+ mod_grid_maxima$max_indices,
#> trnsf_+ local_maxima$local_maxima_vector)
#>
#> trnsf_> # 6) Map GRID points to maximum points (within 4 hours)
#> trnsf_> transform_maxima <- transform_df(grid_result$episode_start, final_maxima)
#>
#> trnsf_> # 7) Redistribute overlapping maxima between GRID points
#> trnsf_> final_between_maxima <- detect_between_maxima(example_data_5_subject, transform_maxima)
#>
#> trnsf_> # Complete pipeline example with larger dataset (example_data_hall)
#> trnsf_> # This demonstrates the same workflow on a more comprehensive dataset
#> trnsf_> hall_threshold <- 130
#>
#> trnsf_> hall_gap <- 60
#>
#> trnsf_> hall_hours <- 2
#>
#> trnsf_> # 1) Find GRID points on larger dataset
#> trnsf_> hall_grid_result <- grid(example_data_hall, gap = hall_gap, threshold = hall_threshold)
#>
#> trnsf_> # 2) Find modified GRID points
#> trnsf_> hall_mod_grid <- mod_grid(example_data_hall,
#> trnsf_+ start_finder(hall_grid_result$grid_vector),
#> trnsf_+ hours = hall_hours,
#> trnsf_+ gap = hall_gap)
#>
#> trnsf_> # 3) Find maximum points after mod_grid
#> trnsf_> hall_mod_grid_maxima <- find_max_after_hours(example_data_hall,
#> trnsf_+ start_finder(hall_mod_grid$mod_grid_vector),
#> trnsf_+ hours = hall_hours)
#>
#> trnsf_> # 4) Identify local maxima
#> trnsf_> hall_local_maxima <- find_local_maxima(example_data_hall)
#>
#> trnsf_> # 5) Find new maxima
#> trnsf_> hall_final_maxima <- find_new_maxima(example_data_hall,
#> trnsf_+ hall_mod_grid_maxima$max_indices,
#> trnsf_+ hall_local_maxima$local_maxima_vector)
#>
#> trnsf_> # 6) Transform data
#> trnsf_> hall_transform_maxima <- transform_df(hall_grid_result$episode_start, hall_final_maxima)
#>
#> trnsf_> # 7) Detect between maxima
#> trnsf_> hall_final_between_maxima <- detect_between_maxima(example_data_hall, hall_transform_maxima)These 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.