Metrics-summary

# Data Processing
library(bp)
data("bp_jhs")  # The dataset includes HBPM measurements from a pilot study in 2019 
                # within a single subject during sustained aerobic (endurance) exercise.

# process_data() helps to pre-process the user's input data into
# a format that can be used by other bp package functions:
data_processed <- process_data(
  bp_jhs,
  bp_type = "abpm",
  ap = NULL,
  time_elap = NULL,
  sbp = 'Sys.mmHg.',
  dbp = 'Dias.mmHg.',
  date_time = 'DateTime',
  id = NULL,
  group = NULL,
  wake = NULL,
  visit = NULL,
  hr = 'Pulse.bpm.',
  pp = NULL,
  map = NULL,
  rpp = NULL,
  DoW = NULL,
  ToD_int = NULL,
  eod = NULL,
  data_screen = TRUE,
  SUL = 240,
  SLL = 50,
  DUL = 140,
  DLL = 40,
  HRUL = 220,
  HRLL = 27,
  inc_low = TRUE,
  inc_crisis = TRUE,
  agg = FALSE,
  agg_thresh = 3,
  collapse_df = FALSE,
  dt_fmt = "ymd HMS",
  chron_order = FALSE,
  tz = "UTC"
)

# clinical trial Metrics
library(plyr)

# Time-Dependent Dispersion Metrics
each(bp_arv, bp_sv)(data_processed)    # each() combines functions into one
                                       # bp_arv() returns average real variability, and
                                       # bp_sv() calculates the values of the successive variation

# Time-Independent Dispersion Metrics
each(p_mag, bp_range, bp_cv)(data_processed)  # p_mag() returns the magnitude (peak and trough) of the BP, 
                                              # bp_range() displays the BP range, and
                                              # where bp_cv() calculates the coefficient of variation

# Sleep-dependent Metrics
each(dip_calc, bp_sleep_metrics)(data_processed)  # dip_calc() returns nocturnal dipping % & classification, and
                                                  # bp_sleep_metrics() shows the BP sleep metrics