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Type:	Package
Title:	Blood Pressure Analysis in R
Version:	2.1.1
Maintainer:	John Schwenck <jschwenck12@gmail.com>
Description:	A comprehensive package to aid in the analysis of blood pressure data of all forms by providing both descriptive and visualization tools for researchers.
License:	GPL-3
URL:	https://github.com/johnschwenck/bp
BugReports:	https://github.com/johnschwenck/bp/issues
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.3
Imports:	dplyr, tidyr, magrittr, lubridate, ggplot2, gridExtra,cowplot, tibble, tidyselect, gtable
Depends:	R (≥ 3.5.0)
Suggests:	knitr, rmarkdown, png
VignetteBuilder:	knitr
Language:	en-US
NeedsCompilation:	no
Packaged:	2025-11-21 16:26:30 UTC; jschw
Author:	John Schwenck [aut, cre], Irina Gaynanova [aut]
Repository:	CRAN
Date/Publication:	2025-11-21 20:20:07 UTC

Pipe operator

Description

Seemagrittr::%>% for details.

Usage

lhs %>% rhs

Average Real Variability (ARV)

Description

THIS IS A DEPRECATED FUNCTION. USE bp_arv INSTEAD.

Usage

arv(  data,  inc_date = FALSE,  subj = NULL,  bp_type = 0,  add_groups = NULL,  inc_wake = TRUE)

Arguments

Details

Calculate the Average Real Variability (ARV) at various levels of granularitybased on what is supplied (ID, VISIT, WAKE, and / or DATE). ARV is a measureof dispersion that takes into account the temporal structure of the data and relieson the sum of absolute differences in successive observations, unlike thesuccessive variation (SV) which relies on the sum of squared differences.

Value

A tibble object with a row corresponding to each subject, or alternativelya row corresponding to each date, if inc_date = TRUE. The resulting tibble consists of:

• ID: The unique identifier of the subject. For single-subject datasets, ID = 1

• VISIT: (If applicable) Corresponds to the visit # of the subject, if more than 1

• WAKE: (If applicable) Corresponds to the awake status of the subject (0 = asleep |1 = awake)

• ARV_SBP /ARV_DBP: Calculates the average of the absolute differences betweensuccessive measurements. The resulting value averages across the granularity groupingfor however many observations are present.

• N: The number of observations for that particular grouping. Ifinc_date = TRUE,N corresponds to the number of observations for that date. Ifinc_date = FALSE, Ncorresponds to the number of observations for the most granular grouping available (i.e.a combination ofID,VISIT, andWAKE)

• Any add_groups variables supplied to function argument will be present as a column in theresulting tibble.

References

Mena et al. (2005) A reliable index for the prognostic significance ofblood pressure variabilityJournal of Hypertension23(5).505-11,doi:10.1097/01.hjh.0000160205.81652.5a.

Examples

# Load datadata(bp_hypnos)data(bp_jhs)# Process bp_hypnoshypnos_proc <- process_data(bp_hypnos, sbp = "SYST", dbp = "DIAST", date_time = "date.time",id = "id", wake = "wake", visit = "visit", hr = "hr", pp ="pp", map = "map", rpp = "rpp")# Process bp_jhs datajhs_proc <- process_data(bp_jhs, sbp = "Sys.mmHg.", dbp = "Dias.mmHg.", date_time = "DateTime",hr = "Pulse.bpm.")## Not run: # ARV Calculationbp_arv(hypnos_proc, add_groups = c("SBP_Category"))bp_arv(jhs_proc, inc_date = TRUE)## End(Not run)

Average Real Variability (ARV)

Description

Calculate the Average Real Variability (ARV) at various levels of granularitybased on what is supplied (ID, VISIT, WAKE, and / or DATE). ARV is a measureof dispersion that takes into account the temporal structure of the data and relieson the sum of absolute differences in successive observations, unlike thesuccessive variation (SV) which relies on the sum of squared differences.

Usage

bp_arv(  data,  inc_date = FALSE,  subj = NULL,  bp_type = c("both", "sbp", "dbp"),  add_groups = NULL,  inc_wake = TRUE)

Arguments

Value

A tibble object with a row corresponding to each subject, or alternativelya row corresponding to each date, if inc_date = TRUE. The resulting tibble consists of:

• ID: The unique identifier of the subject. For single-subject datasets, ID = 1

• VISIT: (If applicable) Corresponds to the visit # of the subject, if more than 1

• WAKE: (If applicable) Corresponds to the awake status of the subject (0 = asleep |1 = awake)

• ARV_SBP /ARV_DBP: Calculates the average of the absolute differences betweensuccessive measurements. The resulting value averages across the granularity groupingfor however many observations are present.

• N: The number of observations for that particular grouping. Ifinc_date = TRUE,N corresponds to the number of observations for that date. Ifinc_date = FALSE, Ncorresponds to the number of observations for the most granular grouping available (i.e.a combination ofID,VISIT, andWAKE)

• Any add_groups variables supplied to function argument will be present as a column in theresulting tibble.

References

Mena et al. (2005) A reliable index for the prognostic significance ofblood pressure variabilityJournal of Hypertension23(5).505-11,doi:10.1097/01.hjh.0000160205.81652.5a.

Examples

# Load datadata(bp_hypnos)data(bp_jhs)# Process bp_hypnoshyp_proc <- process_data(bp_hypnos, sbp = "SYST", dbp = "DIAST", date_time = "date.time",id = "id", wake = "wake", visit = "visit", hr = "hr", pp ="pp", map = "map", rpp = "rpp")# Process bp_jhs datajhs_proc <- process_data(bp_jhs, sbp = "Sys.mmHg.", dbp = "Dias.mmHg.", date_time = "DateTime",hr = "Pulse.bpm.")# ARV Calculationbp_arv(hyp_proc, add_groups = c("SBP_Category"), bp_type = 'sbp')bp_arv(jhs_proc, inc_date = TRUE)

Measures of Center (bp_center)

Description

Calculate the mean and median at various levels of granularitybased on what is supplied (ID, VISIT, WAKE, and / or DATE) for either SBP,DBP, or both.

Usage

bp_center(  data,  inc_date = FALSE,  subj = NULL,  bp_type = c("both", "sbp", "dbp"),  add_groups = NULL,  inc_wake = TRUE)

Arguments

Value

A tibble object with a row corresponding to each subject, or alternativelya row corresponding to each date if inc_date = TRUE. The resulting tibble consists of:

• ID: The unique identifier of the subject. For single-subject datasets, ID = 1

• VISIT: (If applicable) Corresponds to the visit # of the subject, if more than 1

• WAKE: (If applicable) Corresponds to the awake status of the subject (0 = asleep |1 = awake)

• SBP_mean /DBP_mean: Calculates the mean of systolic blood pressure readingsfor the specified time granularity.

• SBP_med /DBP_med: Calculates the median of systolic blood pressure readingsfor the specified time granularity.

• N: The number of observations for that particular grouping. Ifinc_date = TRUE,N corresponds to the number of observations for that date. Ifinc_date = FALSE, Ncorresponds to the number of observations for the most granular grouping available (i.e.a combination ofID,VISIT, andWAKE)

• Any add_groups variables supplied to function argument will be present as a column in theresulting tibble.

Examples

# Load datadata(bp_hypnos)data(bp_jhs)# Process bp_hypnoshyp_proc <- process_data(bp_hypnos, sbp = "SYST", dbp = "DIAST", date_time = "date.time",id = "id", wake = "wake", visit = "visit", hr = "hr", pp ="pp", map = "map", rpp = "rpp")# Process bp_jhs datajhs_proc <- process_data(bp_jhs, sbp = "Sys.mmHg.", dbp = "Dias.mmHg.", date_time = "DateTime",hr = "Pulse.bpm.")# BP Center Calculationbp_center(hyp_proc, subj = c(70417, 70435))

B-Proact1v Children Data

Description

Thebp_children data set contains information on 1,283 children from Bristol, UK,who each took three blood pressure readings per visit over the course of two observationperiods (at ages 9 and 11) and had their body mass index (BMI), physical activity, andsedentary time information recorded. The study examined how sedentary behavior and physicalactivity affected children progressing through primary school to better understand therelationship between elevated blood pressure in children and its impact on the developmentof cardiovascular disease into adulthood

Usage

bp_children

Format

A data frame containing 15 variables pertaining to blood pressure and physicalactivity as follows:

id

Child ID

reading

Index of the blood pressure reading

sbp

Systolic Blood Pressure (mmHg)

dbp

Diastolic Blood Pressure (mmHg)

gender

1 = Male
2 = Female

hh_educ

Household Highest Education:

1 = Up to GCSE/O level or equiv
2 = A level/NVQ or equiv
3 = Degree/HND or equiv
4 = Higher degree (MSc/PhD) or equiv

visit

Visit #

age

Age in Years

ht

Height (ft)

wt

Weight (lbs)

bmi

Body Mass Index

N.valid.days.all

Number of days of child accelerometer data overall

avg.mins.all

Average minutes per day over all valid days

sed.avg.mins.all

Average sedentary minutes per day over all valid days

mvpa.avg.mins.all

Average minutes of Moderate to Vigorous Physical Activity (MVPA) per day over all valid days

Details

(Solomon-Moore E, Salway R, Emm-Collison L, Thompson JL, Sebire SJ, Lawlor DA, Jago R (PI). 2020).

Licensed under a CC-BY Creative Commons Attribution 4.0 International

Source

Original Paper:doi:10.1371/journal.pone.0232333

Data:doi:10.5281/zenodo.1049587

Principal Investigator (PI): Russ Jago (University of Bristol)

Coefficient of Variation (CV)

Description

Calculate the coefficient of variation at various levels of granularitybased on what is supplied (ID, VISIT, WAKE, and / or DATE) for either SBP,DBP, or both. CV is a measure of dispersion

Usage

bp_cv(  data,  inc_date = FALSE,  subj = NULL,  bp_type = c("both", "sbp", "dbp"),  add_groups = NULL,  inc_wake = TRUE)

Arguments

Value

A tibble object with a row corresponding to each subject, or alternativelya row corresponding to each date if inc_date = TRUE. The resulting tibble consists of:

• ID: The unique identifier of the subject. For single-subject datasets, ID = 1

• VISIT: (If applicable) Corresponds to the visit # of the subject, if more than 1

• WAKE: (If applicable) Corresponds to the awake status of the subject (0 = asleep |1 = awake)

• CV_SBP /CV_DBP: Calculates the ratio of standard deviation to the mean.CV_SBPorCV_DBP is useful for comparing the degree of variation from one data seriesto another.

• SD_SBP /SD_DBP: For completeness, thecv function also includes thestandard deviation as a comparison metric to measure spread around the average.

• N: The number of observations for that particular grouping. Ifinc_date = TRUE,N corresponds to the number of observations for that date. Ifinc_date = FALSE, Ncorresponds to the number of observations for the most granular grouping available (i.e.a combination ofID,VISIT, andWAKE)

• Any add_groups variables supplied to function argument will be present as a column in theresulting tibble.

Examples

# Load datadata(bp_hypnos)data(bp_jhs)# Process bp_hypnoshyp_proc <- process_data(bp_hypnos, sbp = "SYST", dbp = "DIAST", date_time = "date.time",id = "id", wake = "wake", visit = "visit", hr = "hr", pp ="pp", map = "map", rpp = "rpp")# Process bp_jhs datajhs_proc <- process_data(bp_jhs, sbp = "Sys.mmHg.", dbp = "Dias.mmHg.", date_time = "DateTime",hr = "Pulse.bpm.")# CV Calculationbp_cv(hyp_proc, inc_date = TRUE, add_groups = "SBP_Category", bp_type = 'sbp')bp_cv(jhs_proc, add_groups = c("meal_time"))# Notice that meal_time is not a column from process_data, but it still works

Task Shifting and Blood Pressure Control in Ghana Data

Description

Thebp_ghana data set includes 757 subjects across 32 community health centers whowere partitioned into intervention groups according to a pragmatic cluster-randomized trialwith 389 in the health insurance coverage (HIC) group and 368 in another group consistingof a combination of HIC with a nurse-led task-shifting strategy for hypertension control(TASSH) (this group is denoted TASSH + HIC). This study was an effort to assess thecomparative effectiveness of HIC alone versus the combination of TASSH + HIC on reducingsystolic blood pressure among patients with uncontrolled hypertension in Ghana. Baselineblood pressure measurements and 12 month follow-up results were collected among subjects,85% of whom had 12 month data present

Usage

bp_ghana

Format

A data frame containing 21 variables pertaining to blood pressure as follows:

ID

Subject ID

Time_Elapsed

How much time elapsed between readings: Baseline measurement, 6 month follow-up,12 month follow-up

SBP

Systolic Blood Pressure

DBP

Diastolic Blood Pressure

Age

Age in Years

Gender

0 = Male
1 = Female

EducationLevel

Highest Degree Earned:

1 = No Schooling
2 = Primary Schooling (Grades 1 to 6)
3 = Junior Secondary Schooling (JSS) (Grades 7-8)
4 = Secondary School (Grades 9-11)
5 = Completed Secondary School
6 = Technical school certificate
7 = Some college but no degree
8 = Associate degree
9 = Graduate or Professional school (MD, JD, etc.)
999 = Not specified

EmploymentStatus

0 = Unemployed
1 = Employed

Literacy

0 = Illiterate
1 = Literate

Smoking

1 = Smoker
2 = Occasional
3 = Ex-Smoker
4 = Non-Smoker

Income

Income level expressed in home currency - Ghanaian cedi (GhC)

SiteNumber

Site Location in Ghana:

1 = Suntreso
2 = Ananekrom
3 = Manhiya
4 = Apatrapa
5 = Nkawie
6 = Afrancho
7 = Kumsai South
8 = Mampongteng
9 = Mampong
10 = Bomfa
11 = Ejura
12 = Kofiase
13 = Kokofu
14 = Asuofia
15 = Konongo
16 = Subirisu
17 = Effiduase
18 = Abuakwa
19 = Tafo
20 = KMA
21 = Methodist
22 = Kenyasi
23 = Asonamaso
24 = Juansa
25 = Juaso
26 = Sekyredumase
27 = Nkenkaasu
28 = Berekese
29 = Bekwai
30 = Dwease
31 = Kuntanese
32 = Foase

CVRisk

Cardiovascular Risk Assessment (%) at Baseline:

1) <10%
2) 10-20%
3) 20-30%
4) 30 to 40%
999= Missing Data

BMI

Body Mass Index

BMIClassification

Classification of BMI:

1 = Underweight (<18.5 kg/m^2)
2 = Normal (18.5 to 24.9 kg/m^2)
3 = Overweight (25.0 to 29.9 kg/m^2)
4 = Obese (>30 kg/m^2)

PhysicalActivity

All activity in weighted MET minutes (per week) at Baseline

TrtorCtrl

0 = Control
1 = Treatment

RuralUrban

0 = Rural
1 = Urban

DoctorsAvailable

# of doctors on staff

NursesAvailable

# of nurses on staff

Patientsannually

# of patients seen annually

Details

(Ogedegbe G, Plange-Rhule J, Gyamfi J, Chaplin W, Ntim M, Apusiga K, Iwelunmor J, Awudzi KY,Quakyi KN, Mogavero JN, Khurshid K, Tayo B, Cooper R. 2019).

Licensed under a CC-0 1.0 (Creative Commons) Universal Public Domain Dedication License

Source

Original Paper:doi:10.1371/journal.pmed.1002561

Data:doi:10.5061/dryad.16c9m51

Blood Pressure Histograms

Description

Thebp_hist function serves to display the frequencies of theSBPandDBP readings. These histograms are formatted to complement thebp_scatterfunction.

Usage

bp_hist(data, subj = NULL, bins = 30, na.rm = TRUE)

Arguments

Value

A list containing four objects: three histogram visual graphics corresponding tothe SBP / DBP totals, SBP frequency, and DBP frequency, and a fourth list elementcorresponding to a plot legend object for use in thebp_report function

Examples

data("bp_jhs")data("bp_hypnos")hyp_proc <- process_data(bp_hypnos,                         bp_type = 'ABPM',                         sbp = "syst",                         dbp = "DIAST",                         date_time = "date.time",                         id = "id",                         wake = "wake",                         visit = "visit",                         hr = "hr",                         map = "map",                         rpp = "rpp",                         pp = "pp",                         ToD_int = c(5, 13, 18, 23))jhs_proc <- process_data(bp_jhs,                         sbp = "Sys.mmHg.",                         dbp = "Dias.mmHg.",                         date_time = "DateTime",                         hr = "pulse.bpm.")rm(bp_hypnos, bp_jhs)bp_hist(hyp_proc)bp_hist(jhs_proc)

HYPNOS Study - 5 Subject Sample

Description

ABPM measurements for 5 subjects with Type II diabetes. These data are part of a larger study sample thatconsisted of patients with Type 2 diabetes recruited from the general community. To be eligible, patients withType 2 diabetes, not using insulin therapy and with a glycosylated hemoglobin (HbA_1c) value at least 6.5

Usage

bp_hypnos

Format

A data frame with the following columns:

NR.

Integer. The index corresponding to the reading of a particular subject for a given visit.

DATE.TIME

Character. The date-time value corresponding to the given reading.

SYST

Integer. The systolic blood pressure reading.

MAP

Integer. The mean arterial pressure value.

DIAST

Integer. The diastolic blood pressure reading.

HR

Integer. The heart rate value (measured in beats per minute – bpm).

PP

Integer. The pulse pressure value calculated as the systolic value - the diastolic value.

RPP

Integer. The rate pressure product calculated as the systolic reading multiplied by the heart rate value.

WAKE

Integer. A logical indicator value corresponding to whether or not a subject is awake (WAKE = 1) or not.

ID

Integer. A unique identifier for each subject.

VISIT

Integer. A value associated with the visit number or a particular subject.

DATE

Character. A date-valued column indicating the date of the given reading. Dates are specified according to theiractual date (i.e. 01:00 corresponds to the next date, even if the subject is awake. Awake-state is indicated viaWAKE).

Blood Pressure - 1 Subject - John Schwenck

Description

Single-subject self-monitored blood pressure readings over 108 days (April 15, 2019 - August 01, 2019).This data set has been processed and uploaded to the Harvard Dataverse for public use. It containsvariables pertaining to Date/Time, Systolic BP, Diastolic BP, and Heart Rate. The data assumes a thresholdblood pressure of 135 / 85 which is used to calculate excess amounts.

Usage

bp_jhs

Format

A data frame with the following columns:

DateTime

A POSIXct-formatted column corresponding to the date and time of the corresponding reading in local time.

Month

Integer. The month corresponding to the Date column.

Day

Integer. The day of the month corresponding to the Date column.

Year

Integer. The year corresponding to the Date column.

DayofWk

Character. The day of the week corresponding to the Date column.

Hour

Integer. The hour corresponding to the DateTime column.

Meal_Time

Character. The estimated meal time corresponding to the DateTime column.

Sys.mmHg.

Integer. The systolic blood pressure reading.

Dias.mmHg.

Integer. The diastolic blood pressure reading.

bpDelta

Integer. The difference between the Sys.mmHg. and Dias.mmHg. column. This is also known as the Pulse Pressure.

Pulse.bpm.

Integer. The heart rate value (measured in beats per minute – bpm).

Source

doi:10.7910/DVN/EA1SAP

Blood Pressure Magnitude (Peak and Trough)

Description

Calculate the Peak and Trough defined as the max BP - average BP andaverage BP - min BP, respectively.

Usage

bp_mag(  data,  inc_date = FALSE,  subj = NULL,  bp_type = c("both", "sbp", "dbp"),  add_groups = NULL,  inc_wake = TRUE)

Arguments

Value

A tibble object with a row corresponding to each subject, or alternativelya row corresponding to each date if inc_date = TRUE. The resulting tibble consists of:

• ID: The unique identifier of the subject. For single-subject datasets, ID = 1

• VISIT: (If applicable) Corresponds to the visit # of the subject, if more than 1

• WAKE: (If applicable) Corresponds to the awake status of the subject (0 = asleep |1 = awake)

• Peak_SBP /Peak_DBP: Measures the difference between the max value and the average

• Trough_SBP /Trough_DBP: Measures the difference between the average and themin value

• N: The number of observations for that particular grouping. Ifinc_date = TRUE,N corresponds to the number of observations for that date. Ifinc_date = FALSE,Ncorresponds to the number of observations for the most granular grouping available (i.e.a combination ofID,VISIT, andWAKE)

• Any add_groups variables supplied to function argument will be present as a column in theresulting tibble.

Examples

# Load datadata(bp_hypnos)data(bp_jhs)# Process bp_hypnoshyp_proc <- process_data(bp_hypnos, sbp = "SYST", dbp = "DIAST", date_time = "date.time",id = "id", wake = "wake", visit = "visit", hr = "hr", pp ="pp", map = "map", rpp = "rpp")# Process bp_jhs datajhs_proc <- process_data(bp_jhs, sbp = "Sys.mmHg.", dbp = "Dias.mmHg.", date_time = "DateTime",hr = "Pulse.bpm.")# BP Magnitude Calculationbp_mag(hyp_proc)bp_mag(jhs_proc, inc_date = TRUE)

Pregnancy Day Assessment Clinic Data

Description

Thebp_preg data set includes 209 women each with 30 minute recordings during the Pregnancy DayAssessment Clinic (PDAC) observation window for up to a maximum of 240 minutes (i.e. a maximumof 8 total readings per subject per observation window in addition to an initial "booking" readingbefore the PDAC assessment). This recent study in obstetrics and gynecology investigatedpregnancy-induced hypertension (PIH) and pre-eclampsia (PE) prediction by analyzing cardiac andphysiological information to determine whether the blood pressure assessment of the first observationwindow of 1 hour (60 minutes) was sufficient relative to the standard 4 hour (240 minute) window.

Usage

bp_preg

Format

A data frame containing 55 variables related to physiological assessment during pregnancy.The data variable dictionary is as follows:

Abdominal.pain

Binary indicator for whether an individual exhibits abdominal pain (1 = yes) or not (0 = no)

Accelerations

Fetal heart rate accelerations

Admission

Admission from PDAC

Age

Maternal age at estimated date of confinement

ALP

Alkaline phosphatase u/L

ALT

Alanaine transaminase u/L

AN_PET

Binary indicator for Antenatal diagnosis of pre-eclampsia 1 = yes, 0 = no

Antihypertensive.Meds

Current antihypertensive medication

Asymptomatic

Binary indicator for whether an individual is asymptomatic (1 = yes) or not (0 = no)

Baseline

Baseline fetal heart rate (normal, bradycardia, tachycardia)

BMI

Body Mass Index

Creatinine

micromol/L

CTG

Cardiotocogram

D_att_Dryad

Date of attendance modified by adding a random number of days between -7 and +7

DBP

Diastolic Blood Pressure

Decelerations

Fetal heart rate decelerations

DOB_Dryad

Maternal date of birth modified by adding a random number of days between -7 and +7

DrRV

Medical review during PDAC

EDC_Dryad

Estimated date of confinement modified by adding a random number of days between -7 and +7

EmCS

Emergency Caesarean section

Final.Diagnosis.

Final diagnosis: 0 = nil, 1 = pregnancy induced hypertension, 2 = pre-eclampsia

GA_att

Gestational age (weeks) at attendance

GGT

Gamma glutamyl transaminase u/L

Grav

Gravidity: number of pregnancies including the current one

Headache

Binary indicator for whether an individual has a headache (1 = yes) or not (0 = no)

Ht

Maternal Height (cm)

Hyperreflexia

Binary indicator for whether an individual exhibits Hyperreflexia (1 = yes) or not (0 = no)

IOL

Induction of labour

ID

Subject ID #

IP_PET

Intrapartum diagnosis of pre-eclampsia 1 = yes, 0 = no

Nausea

Binary indicator for whether an individual exhibits Nausea (1 = yes) or not (0 = no)

Oedema

Binary indicator for whether an individual exhibits Oedema (1 = yes) or not (0 = no)

Para

Parity: number of pregnancies proceding to 20 or more weeks, not including the current one

PHx_ASA

Prescribed aspirin in this pregnancy

PHx_Eclampsia

Past history of eclampsia

PHx_EssBP

Past history of essential hypertension

PHx_PET

Past history of pre-eclampsia

PHx_Smoker

Smoking history

PIERS

Pre-eclampsia Integrated Estimate of Risk (%)

PN_PET

Postnatal diagnosis of pre-eclampsia 1 = yes, 0 = no

Platelets

10^9 per mL

PrevPDAC

Number of previous PDAC assessments this pregnancy

Privacy

Generic privacy consent form permits participation in audit

SBP

Systolic Blood Pressure

SOB

Binary indicator for whether an individual exhibits Shortness of Breath (SOB) (1 = yes) or not (0 = no)

Safe.for.discharge.at.1.hour.

Binary indicator for whether an individual is safe for discharge at 1 hour(1 = yes) or not (0 = no)

Safe.for.discharge.at.1.hour....Other..please.specify.

Binary indicator for whether an individual is safefor discharge at 1 hour - other - comments

Self.discharge

Self-discharge from PDAC

Time_Elapsed

30 minute recordings during the Pregnancy Day Assessment Clinic (PDAC) observationwindow for up to a maximum of 240 minutes including the "Booking" recording

Urate

mmol/L

Urea

micromol/L

UrinePCR

Spot urine protein:creatinine ratio mg/mmol

Variability

Fetal heart rate variability as judged visually

Visual.Disturbances

Binary indicator for whether an individual exhibits Visual Disturbances (1 = yes)or not (0 = no)

Wt

Pre-pregnancy or early pregnancy weight (kg)

Details

(McCarthy EA, Carins TA, Hannigan Y, Bardien N, Shub A, Walker S. 2015)

Licensed under a CC-0 1.0 (Creative Commons) Universal Public Domain Dedication License

Source

doi:10.5061/dryad.0bq15

Blood Pressure Range

Description

Calculates the range (max - min) of both SBP and DBP values in addition to max andmin values for reference with the option to specify date as an additional levelof granularity

Usage

bp_range(  data,  inc_date = FALSE,  subj = NULL,  add_groups = NULL,  inc_wake = TRUE)

Arguments

Value

A tibble with SBP_max, SBP_min, SBP_range, DBP_max, DBP_min, DBP_rangeand any additional optional columns included in data such asID,VISIT,WAKE, andDATE. If inc_date = TRUE, each row will correspond to a date.The resulting tibble consists of:

• ID: The unique identifier of the subject. For single-subject datasets, ID = 1

• VISIT: (If applicable) Corresponds to the visit # of the subject, if more than 1

• WAKE: (If applicable) Corresponds to the awake status of the subject (0 = asleep |1 = awake)

• SBP_max /DBP_max: Finds the maximum value for the given grouping granularity

• SBP_min /DBP_min: Finds the minimum value for the given grouping granularity

• SBP_range /DBP_range: Calculates the range between the max and min values

• N: The number of observations for that particular grouping. Ifinc_date = TRUE,N corresponds to the number of observations for that date. Ifinc_date = FALSE,N corresponds to the number of observations for the most granular grouping available(i.e. a combination ofID,VISIT, andWAKE)

• Any add_groups variables supplied to function argument will be present as a column in theresulting tibble.

Examples

# Load bp_hypnosdata(bp_hypnos)data(bp_jhs)# Process bp_hypnoshypnos_proc <- process_data(bp_hypnos, sbp = "SYST", dbp = "DIAST", date_time = "date.time",id = "id", wake = "wake", visit = "visit", hr = "hr", pp ="pp", map = "map", rpp = "rpp")# Process bp_jhs datajhs_proc <- process_data(bp_jhs, sbp = "Sys.mmHg.", dbp = "Dias.mmHg.", date_time = "DateTime",hr = "Pulse.bpm.")# Calculate BP rangebp_range(hypnos_proc)bp_range(jhs_proc, inc_date = TRUE, add_groups = c("meal_time"))# Notice that meal_time is not a column from process_data, but it still works

Blood Pressure in Salt-Sensitive Dahl Rats Data

Description

Thebp_rats data set includes arterial blood pressure waveform time series dataof the SS(n = 9) and SS.13 (n = 6) genetic strains of Dahl rats sampled at 100 Hz whowere each administered a low and high salt diet. It is taken from the study of Bugenhagenet al.(2010) which sought to investigate the origins of the baroreflex dysfunction insalt-sensitive Dahl rats on Hypertension.

Usage

bp_rats

Format

A data frame containing 5 variables pertaining to arterial continuous waveform bloodpressure as follows:

rat_type

The type of a particular Dahl Rat (either SS or SSBN13) corresponding to the sodium intake administered:

ss_hs = SS rat given the high sodium diet
ss_ls = SS rat given the low sodium diet
ssbn13_hs = SSBN13 rat given the high sodium diet
ssbn13_ls = SSBN13 rat given the low sodium diet

rat_ID

ID # corresponding to a particular rat. There are 9 SS rats and 6 SSBN13 rats.

ABP

Continuous waveform data of each rat's arterial (blood) pressure sampled at 100 Hz.

time_sec

Amount of time elapsed, expressed in seconds

time_min

Amount of time elapsed, expressed in minutes

Details

(Goldberger A., Amaral L., Glass L., Hausdorff J., Ivanov P. C., Mark R., Bugenhagen S.M.,Cowley A.W. Jr, Beard D.A., ... & Stanley H. E. 2000).

Licensed under a ODC-BY (Creative Commons) Open Data Commons Attribution License 1.0

Source

Original Paper:doi:10.1152/physiolgenomics.00027.2010

Data:doi:10.13026/C20597

Blood Pressure Report

Description

Thebp_report function serves to aggregate various data visuals and metricspertaining to the supplied data set into a clean formatted report.

Usage

bp_report(  data,  subj = NULL,  inc_low = TRUE,  inc_crisis = TRUE,  group_var = NULL,  save_report = TRUE,  path = NULL,  filename = "bp_report",  width = 11,  height = 8.5,  filetype = "pdf",  units = "in",  scale = 1.25,  plot = TRUE,  hist_bins = 30)

Arguments

Value

Ifplot = TRUE, the function produces a plot of BP report that contains scatterplot of BP values by stages (seebp_scatter), histograms of BP values by stages (seebp_hist) and frequency tables of BP values by stages and day of the week/time of the day (seedow_tod_plots). Ifplot = FALSE, the function returns the grob object that can be plotted later usinggrid.arrange. Ifsave_report = TRUE, the report will be automatically saved at the current working directory (can be checked usinggetwd()) or at specified file path.

Examples

data("bp_jhs")data("bp_hypnos")hyp_proc <- process_data(bp_hypnos,                         sbp = "syst",                         dbp = "DIAST",                         date_time = "date.time",                         id = "id",                         wake = "wake",                         visit = "visit",                         hr = "hr",                         map = "map",                         rpp = "rpp",                         pp = "pp",                         ToD_int = c(5, 13, 18, 23))jhs_proc <- process_data(bp_jhs,                         sbp = "Sys.mmHg.",                         dbp = "Dias.mmHg.",                         date_time = "DateTime",                         hr = "pulse.bpm.")rm(bp_hypnos, bp_jhs)## Not run: # Single-subject Report# save_report = FALSE for illustrative purposes# plot = TRUE will automatically generate a plotbp_report(jhs_proc, save_report = FALSE, plot = TRUE)# Multi-subject Report# save_report = FALSE for illustrative purposes# plot = FALSE will suppress the plot output and return a grob objectout = bp_report(hyp_proc, group_var = 'VISIT', save_report = FALSE, plot = FALSE)gridExtra::grid.arrange(out)## End(Not run)

Blood Pressure Stage Scatter Plot

Description

Display allSBP andDBPreadings on a scatterplot with deliniation of BP according to the 8mutually exclusive levels of Hypertension as in Lee et al (2020) (the default), or thelevels set by the American Heart Association (AHA).

Usage

bp_scatter(  data,  plot_type = c("stages2020", "AHA"),  subj = NULL,  group_var = NULL,  wrap_var = NULL,  inc_crisis = TRUE,  inc_low = TRUE,  bp_cutoffs = list(c(100, 120, 130, 140, 180), c(60, 80, 80, 90, 120)),  bp_type = c("hbpm", "abpm", "ap"))

Arguments

Details

There are eight total stages according to Lee et al (2020) with the optionsto include two additional categories for "Low" (Hypotension) and Hypertensive "Crisis". Thecategories are as follows:

• Low - (Optional) Legacy category for consistency with AHA stages. According to the AHA, low blood pressure is any reading with SBP < 100 and DBP < 60, and is depicted in light blue in the scatter plot. This is always displayed in"AHA" plot, and can be displayed in"stages2020" plot by settinginc_low = TRUE.

• Normal -SBP readings less than 120 andDBP readingsless than 80. Reading within this range that either have SBP > 100 or DBP > 60 are also considered Normal by AHA. Normal BP is depicted in green in the scatter plot.

• Elevated -SBP readings between 120 - 129 andDBPreadings less than 80. Coincides with Elevated stage as defined by AHA. Without intervention to control the condition, individuals are likely to develop Hypertension. Elevated BP is depicted in yellow in the scatter plot.

• Stage 1 - All (SDH) -SBP readings between 130 - 139 andDBPreadings between 80 - 89. Stage 1 Hypertension will typically result in doctors prescribingmedication or lifestyle changes. Stage 1 BP is depicted in dark orange in the scatter plot. These readings correspond to Stage 1 as defined by AHA.

• Stage 1 - Isolated Diastolic Hypertension (IDH) -SBP readingsless than 130, butDBP readings between 80 - 89. This alternative stage 1 level accountsfor unusually high diastolic readings, but fairly normal systolic readings and is depicted inorange in the plot. These readings correspond to Stage 1 as defined by AHA.

• Stage 1 - Isolated Systolic Hypertension (ISH) -SBP readingsbetween 130 - 139, butDBP readings less than 80. This alternative stage 1 level accountsfor unusually high systolic readings, but fairly normal diastolic readings and is depicted inorange in the plot. These readings correspond to Stage 1 as defined by AHA.

• Stage 2 - All (SDH) -SBP readings between 140 - 180 andDBP readingsbetween 90 - 120. Stage 2 Hypertension will typically result in doctors prescribing bothmedication and lifestyle changes. Stage 2 BP is depicted in bright red in the scatter plot. These readings correspond to Stage 2 as defined by AHA.

• Stage 2 - Isolated Diastolic Hypertension (IDH) -SBP readingsless than or equal to 140, butDBP readings greater than or equal to 90. This alternativestage 2 level accounts for unusually high diastolic readings, but fairly normal systolic readingsand is depicted in red. These readings correspond to Stage 2 as defined by AHA.

• Stage 2 - Isolated Systolic Hypertension (IDH) -SBP readingsgreater than or equal to 140, butDBP readings less or equal to 90. This alternativestage 2 level accounts for unusually high systolic readings, but fairly normal diastolic readingsand is depicted in red. These readings correspond to Stage 2 as defined by AHA.

• Crisis - (Optional) Legacy category for consistency with AHA stages. According to the AHA, hypertensive crisis is defined as aSBP reading exceeding 180 or aDBP reading exceeding 120. This stage requires medical attention immediately.Crisis is depicted in red in the scatter plot. This is always displayed in"AHA" plot, and can be displayed in"stages2020" plot by settinginc_crisis = TRUE.

Value

A scatter plot graphic using the ggplot2 package overlaying each reading (represented aspoints) onto a background that contains each stage

References

Lee H, Yano Y, Cho SMJ, Park JH, Park S, Lloyd-Jones DM, Kim HC. Cardiovascular risk of isolatedsystolic or diastolic hypertension in young adults.Circulation. 2020; 141:1778-1786.doi:10.1161/CIRCULATIONAHA.119.044838

Unger, T., Borghi, C., Charchar, F., Khan, N. A., Poulter, N. R., Prabhakaran, D., ... & Schutte,A. E. (2020). 2020 International Society of Hypertension global hypertension practice guidelines.Hypertension, 75(6), 1334-1357.doi:10.1161/HYPERTENSIONAHA.120.15026

Examples

data("bp_jhs")data("bp_hypnos")data("bp_ghana")hypnos_proc <- process_data(bp_hypnos,                         bp_type = 'abpm',                         sbp = "syst",                         dbp = "DIAST",                         date_time = "date.time",                         id = "id",                         wake = "wake",                         visit = "visit",                         hr = "hr",                         map = "map",                         rpp = "rpp",                         pp = "pp",                         ToD_int = c(5, 13, 18, 23))jhs_proc <- process_data(bp_jhs,                         sbp = "Sys.mmHg.",                         dbp = "Dias.mmHg.",                         date_time = "DateTime",                         hr = "pulse.bpm.")rm(bp_hypnos, bp_jhs)# HYPNOS Databp_scatter(hypnos_proc,           inc_crisis = TRUE,           inc_low = TRUE,           group_var = "wake",           wrap_var = "day_of_week")# JHS Databp_scatter(jhs_proc,           plot_type = "AHA",           group_var = "time_of_day")# Ghana Data Set#(Note that column names are of proper naming convention so no processing needed)bp_scatter(bp::bp_ghana, inc_crisis = TRUE, inc_low = FALSE, group_var = "TIME_ELAPSED")

Nocturnal Blood Pressure Metrics

Description

Thebp_sleep_metrics function serves to calculate nocturnal metricsfrom relevant medical literature.

Usage

bp_sleep_metrics(data, subj = NULL)

Arguments

Details

NOTE: Any reference to "sleep" in the bp package refers to an individual's nocturnal period;"sleep" is used in an informal sense for intuitive purposes. Technically, from a clinical perspective,indication of sleep is currently obtained through means of EEG or other highly specialized equipment.For all intents and purposes, sleep in the context of this package refers to actigraphy-inferrednocturnal periods of rest.

The calculation of BP metrics related to sleep is based on averages of BP readings from four periods as identified in Kairo et al. (2003):presleep or evening (2 hours before sleep start),prewake (2 hours before wake),postwake or morning (2 hours after wake),lowest (3 measurements centered at the minimal BP reading over sleep).The function usesWAKE column to automatically allocate BP measurements to various periods. The following metrics are defined as afunction of the period averages (separately for SBP and DBP)

dip_calc = 1 - mean_sleep_BP/mean_wake_BP (dip proportion)

noct_fall = mean_presleep_BP - mean_lowest_BP (nocturnal fall)

ST_mbps = mean_postwake_BP - mean_lowest_BP (sleep through morning blood pressure surge)

PW_mbps = mean_postwake_BP - mean_prewake_BP (prewake morning blood pressure surge)

ME_avg = (mean_presleep_BP + mean_postwake_BP)/2 (morning-evening average)

ME_diff = mean_postwake_BP - mean_presleep_BP (morning-evening difference)

wSD = ( (wake_SD x HRS_wake) + (sleep_SD x HRS_sleep) ) / (HRS_wake + HRS_sleep) (weighted standard deviation)

Value

The function outputs a list containing 4 tibble objects corresponding to the following tables:

References

Kario, K., Pickering, T. G., Umeda, Y., Hoshide, S., Hoshide, Y., Morinari, M., ... & Shimada, K. (2003). Morning surge in bloodpressure as a predictor of silent and clinical cerebrovascular disease in elderly hypertensives: a prospective study. Circulation,107(10), 1401-1406.

Examples

hypnos_proc <- process_data(bp_hypnos,                           sbp = "syst",                           dbp = "DIAST",                           date_time = "date.time",                           id = "id",                           wake = "wake",                           visit = "visit",                           hr = "hr",                           map = "map",                           rpp = "rpp",                           pp = "pp",                           bp_type = "ABPM")bp_sleep_metrics(hypnos_proc)

Blood Pressure Stage Classification

Description

Adds BP_CLASS, SBP_Category, and DBP_Category columns to supplied dataframe.

Usage

bp_stages(  data,  sbp,  dbp,  bp_type = c("hbpm", "abpm", "ap"),  inc_low = TRUE,  inc_crisis = TRUE,  data_screen = TRUE,  SUL = 240,  SLL = 50,  DUL = 140,  DLL = 40,  adj_sbp_dbp = TRUE,  guidelines = c("Lee_2020", "AHA", "Custom"),  bp_cutoffs = list(c(100, 120, 130, 140, 180), c(60, 80, 80, 90, 120)))

Arguments

Details

Supplied dataframe must adhere to the unified format using theprocess_data function.

Value

A dataframe with additional columns corresponding to the stages of blood pressure and thesupplementary SBP / DBP categories

References

Lee H, Yano Y, Cho SMJ, Park JH, Park S, Lloyd-Jones DM, et al. Cardiovascular risk of isolated systolicor diastolic hypertension in young adults.Circulation. 2020;141(22):1778-1786.doi:10.1161/CIRCULATIONAHA.119.044838

Muntner, P., Carey, R. M., Jamerson, K., Wright Jr, J. T., & Whelton, P. K. (2019). Rationale for ambulatory and home blood pressure monitoring thresholds in the 2017 American College of Cardiology/American Heart Association Guideline. Hypertension, 73(1), 33-38.doi:10.1161/HYPERTENSIONAHA.118.11946

Examples

# Load bp_hypnosdata(bp_hypnos)bp_stages(bp_hypnos, sbp = "syst", dbp = "diast")# Load bp_jhs datadata(bp_jhs)bp_stages(bp_jhs, sbp = "sys.mmhg.", dbp = "dias.mmhg.")

Aggregated BP Summary Statistics

Description

Combines the output from the following functions:

• bp_center

• bp_cv

• bp_arv

• bp_sv

• bp_mag

• bp_range

Usage

bp_stats(  data,  inc_date = FALSE,  subj = NULL,  bp_type = c("both", "sbp", "dbp"),  add_groups = NULL,  inc_wake = TRUE)

Arguments

Value

A tibble object with a row corresponding to each subject, or alternativelya row corresponding to each date, if inc_date = TRUE. The resulting tibble consists of:

• ID: The unique identifier of the subject. For single-subject datasets, ID = 1

• N: The number of observations for that particular grouping. Ifinc_date = TRUE,N corresponds to the number of observations for that date. Ifinc_date = FALSE, Ncorresponds to the number of observations for the most granular grouping available (i.e.a combination ofID,VISIT, andWAKE)

• VISIT: (If applicable) Corresponds to the visit # of the subject, if more than 1

• WAKE: (If applicable) Corresponds to the awake status of the subject (0 = asleep |1 = awake)

• Output from the following functions:bp_center,bp_cv,bp_arv,bp_sv,bp_mag,bp_range

• Any add_groups variables supplied to function argument will be present as a column in theresulting tibble.

Examples

# Load datadata(bp_hypnos)data(bp_jhs)# Process bp_hypnoshyp_proc <- process_data(bp_hypnos, sbp = "SYST", dbp = "DIAST", date_time = "date.time",id = "id", wake = "wake", visit = "visit", hr = "hr", pp ="pp", map = "map", rpp = "rpp")# Process bp_jhs datajhs_proc <- process_data(bp_jhs, sbp = "Sys.mmHg.", dbp = "Dias.mmHg.", date_time = "DateTime",hr = "Pulse.bpm.")# BP Stats Aggregated Tablebp_stats(hyp_proc, subj = c(70417, 70435), add_groups = c("SBP_Category"), bp_type = 'sbp')bp_stats(jhs_proc, add_groups = c("SBP_Category"))

Successive Variation (SV)

Description

Calculate the successive variation (SV) at various levels of granularitybased on what is supplied (ID, VISIT, WAKE, and / or DATE)for either SBP,DBP, or both. SV is a measure of dispersion that takes into account thetemporal structure of the data and relies on the sum of squared differencesin successive observations, unlike the average real variability (ARV)which relies on the sum of absolute differences.

SV = \sqrt{\frac{\sum(x_{i+1} - x_i)^2}{n-1}}

Usage

bp_sv(  data,  inc_date = FALSE,  subj = NULL,  bp_type = c("both", "sbp", "dbp"),  add_groups = NULL,  inc_wake = TRUE)

Arguments

Details

NOTE: The canonical standard deviation, independent of the temporalstructure using the sample average, is added for comparison:

SD = \sqrt{\frac{\sum(x_{i+1} - \bar{x})^2}{n-1}}

Value

A tibble object with a row corresponding to each subject, or alternativelya row corresponding to each date if inc_date = TRUE. The resulting tibble consists of:

• ID: The unique identifier of the subject. For single-subject datasets, ID = 1

• VISIT: (If applicable) Corresponds to the visit # of the subject, if more than 1

• WAKE: (If applicable) Corresponds to the awake status of the subject (0 = asleep |1 = awake)

• SV_SBP /SV_DBP: Calculates the square root of the average squared differencesbetween successive measurements. The resulting value averages across the granularitygrouping for however many observations are present.

• N: The number of observations for that particular grouping. Ifinc_date = TRUE,N corresponds to the number of observations for that date. Ifinc_date = FALSE, Ncorresponds to the number of observations for the most granular grouping available (i.e.a combination ofID,VISIT, andWAKE)

• Any add_groups variables supplied to function argument will be present as a column in theresulting tibble.

Examples

# Load datadata(bp_hypnos)data(bp_jhs)# Process bp_hypnoshypnos_proc <- process_data(bp_hypnos, sbp = "SYST", dbp = "DIAST", date_time = "date.time",id = "id", wake = "wake", visit = "visit", hr = "hr", pp ="pp", map = "map", rpp = "rpp")# Process bp_jhs datajhs_proc <- process_data(bp_jhs, sbp = "Sys.mmHg.", dbp = "Dias.mmHg.", date_time = "DateTime",hr = "Pulse.bpm.")# SV Calculationbp_sv(hypnos_proc)bp_sv(jhs_proc, add_groups = c("meal_time"))# Notice that meal_time is not a column from process_data, but it still works

Blood Pressure Tables

Description

Generate a list of pertinent table outputs that detail various informationspecific to blood pressure.

Usage

bp_tables(  data,  bp_type = c("both", "sbp", "dbp"),  bp_perc_margin = NULL,  wake_perc_margin = 2,  subj = NULL)

Arguments

Value

A list of table outputs for various subsets of the data based on which bp_type is selected

Examples

data("bp_jhs")data("bp_hypnos")hyp_proc <- process_data(bp_hypnos,                         bp_type = 'ABPM',                         sbp = "syst",                         dbp = "DIAST",                         date_time = "date.time",                         id = "id",                         wake = "wake",                         visit = "visit",                         hr = "hr",                         map = "map",                         rpp = "rpp",                         pp = "pp",                         ToD_int = c(5, 13, 18, 23))jhs_proc <- process_data(bp_jhs,                         sbp = "Sys.mmHg.",                         dbp = "Dias.mmHg.",                         date_time = "DateTime",                         hr = "pulse.bpm.")rm(bp_hypnos, bp_jhs)bp_tables(jhs_proc)bp_tables(hyp_proc)

Blood Pressure Time Series Plots

Description

Blood Pressure Time Series Plots

Usage

bp_ts_plots(  data,  index = NULL,  subj = NULL,  first_hour = 0,  rotate_xlab = FALSE,  wrap_var = NULL,  wrap_row = NULL,  wrap_col = NULL,  method = NULL,  formula = NULL)

Arguments

Value

If the data does not contain a DATE_TIME column, a single list will bereturned with the time-dependent plots for each subject ID. If the data does contain a DATE_TIMEcolumn (and index is not specified), a list of two lists will be returned for each subject ID:one corresponding to the time-dependent plots (according to the DATE_TIME values), and anotherplot corresponding to the HOUR plots which show repeated measurements of BP values throughouta 24-hour period. The index of the output therefore corresponds to whether there is only thetime-dependent plot type (the former situation) or there are both time-dependent and hourlyplot types (the latter situation).

Examples

# Pregnancy Data Set# bp_preg requires the use of the index argument since there are no DATE or# DATE_TIME columns availabledata_preg <- bp::bp_pregdata_preg$Time_Elapsed <- factor(data_preg$Time_Elapsed,     levels = c("Booking", "0", "30", "60", "90", "120", "150", "180", "210", "240"))bp::bp_ts_plots(data_preg, index = 'time_elapsed', subj = 1:3)# JHS Data Set# bp_jhs returns two lists since there is a DATE_TIME column: one for# DATE_TIME and one for HOURdata_jhs <- bp::process_data(bp::bp_jhs,                                    sbp = 'sys.mmhg.',                                    dbp = 'dias.mmhg.',                                     hr = 'pulse.bpm.',                              date_time = 'datetime')bp::bp_ts_plots(data_jhs)# HYPNOS Data Set# bp_hypnos wraps the plots by the visit # since each subject was recorded over# the course of two office visitsdata_hypnos <- bp::process_data(bp::bp_hypnos,                                          sbp = 'syst',                                          dbp = 'diast',                                    date_time = 'date.time')bp::bp_ts_plots(data_hypnos, wrap_var = 'visit', subj = '70435')

Create Groups for Dplyr

Description

Used in the following functions:arv,bp_mag,bp_range,cv,sv,bp_center,bp_stats

Usage

create_grps(data, inc_date, add_groups, inc_wake)

Arguments

Value

A vector of string values corresponding to the column names thatwill subset / group the data in dplyr functions

Coefficient of Variation (CV)

Description

THIS IS A DEPRECATED FUNCTION. USE bp_cv INSTEAD.

Usage

cv(  data,  inc_date = FALSE,  subj = NULL,  bp_type = 0,  add_groups = NULL,  inc_wake = TRUE)

Arguments

Details

Calculate the coefficient of variation at various levels of granularitybased on what is supplied (ID, VISIT, WAKE, and / or DATE) for either SBP,DBP, or both. CV is a measure of dispersion

Value

A tibble object with a row corresponding to each subject, or alternativelya row corresponding to each date if inc_date = TRUE. The resulting tibble consists of:

• ID: The unique identifier of the subject. For single-subject datasets, ID = 1

• VISIT: (If applicable) Corresponds to the visit # of the subject, if more than 1

• WAKE: (If applicable) Corresponds to the awake status of the subject (0 = asleep |1 = awake)

• CV_SBP /CV_DBP: Calculates the ratio of standard deviation to the mean.CV_SBPorCV_DBP is useful for comparing the degree of variation from one data seriesto another.

• SD_SBP /SD_DBP: For completeness, thecv function also includes thestandard deviation as a comparison metric to measure spread around the average.

• N: The number of observations for that particular grouping. Ifinc_date = TRUE,N corresponds to the number of observations for that date. Ifinc_date = FALSE, Ncorresponds to the number of observations for the most granular grouping available (i.e.a combination ofID,VISIT, andWAKE)

• Any add_groups variables supplied to function argument will be present as a column in theresulting tibble.

Examples

# Load datadata(bp_hypnos)data(bp_jhs)# Process bp_hypnoshypnos_proc <- process_data(bp_hypnos, sbp = "SYST", dbp = "DIAST", date_time = "date.time",id = "id", wake = "wake", visit = "visit", hr = "hr", pp ="pp", map = "map", rpp = "rpp")# Process bp_jhs datajhs_proc <- process_data(bp_jhs, sbp = "Sys.mmHg.", dbp = "Dias.mmHg.", date_time = "DateTime",hr = "Pulse.bpm.")## Not run: # CV Calculationbp_cv(hypnos_proc, inc_date = TRUE, add_groups = "SBP_Category")bp_cv(jhs_proc, add_groups = c("meal_time"))# Notice that meal_time is not a column from process_data, but it still works## End(Not run)

Nocturnal Blood Pressure Dipping Calculation

Description

Calculate the percent and average decline (or potentially reverse) in nocturnal blood pressure.This function is typically used with ABPM data, or at minimum, data with a corresponding aWAKE column available to indicate awake vs asleep.

Nocturnal blood pressure decline is an indicator of an individual's natural circadian rhythm. Studieshave shown that individuals with diminished circadian rhythms are more likely to exhibit target organ damage.There is a "U-shaped" relationship that exists among the magnitude of nocturnal blood pressure decline; theextreme dippers and the non-dippers (including reverse dippers) are both more prone to mortality risk than normal dippers.

NOTE: Any reference to "sleep" in the bp package refers to an individual's nocturnal period;"sleep" is used in an informal sense for intuitive purposes. Technically, from a clinical perspective,indication of sleep is currently obtained through means of EEG or other highly specialized equipment.For all intents and purposes, sleep in the context of this package refers to actigraphy-inferrednocturnal periods of rest.

Usage

dip_calc(  data,  sleep_start_end = NULL,  dip_thresh = 0.1,  extreme_thresh = 0.2,  inc_date = FALSE,  subj = NULL)

Arguments

Value

A list containing 2 tibble objects. The first tibble object lists grouped average values for SBP and DBPfor awake and asleep periods. The seconddip_pct tibble object lists the dipping percentage andclassification according to the results from the firstdip tibble. If inc_date = TRUE these twotibbles will be broken down further by date. There are 4 classifications a subject can have (assuming adefault dipping threshold of 10% and extreme dipping threshold of 20% according to the original source):

• Reverse Dipper - no nocturnal decline (greater or equal to 0%)

• Non-Dipper - a nocturnal decline between 0 - 10%

• Dipper - a nocturnal decline between 10% and the extreme dipping % (20%)

• Extreme Dipper - a nocturnal decline exceeding 20%

References

Okhubo, T., Imai, Y., Tsuji, K., Nagai, K., Watanabe, N., Minami, J., Kato, J., Kikuchi, N., Nishiyama, A.,Aihara, A., Sekino, M., Satoh, H., and Hisamichi, S. (1997). Relation Between Nocturnal Decline in BloodPressure and Mortality: The Ohasama Study,American Journal of Hypertension10(11), 1201–1207,doi:10.1016/S0895-7061(97)00274-4.

Examples

## Load bp_hypnosdata(bp_hypnos)## Process bp_hypnoshypnos_proc <- process_data(bp_hypnos,                     sbp = 'syst',                     dbp = 'diast',                     date_time = 'date.time',                     hr = 'hr',                     pp = 'PP',                     map = 'MaP',                     rpp = 'Rpp',                     id = 'id',                     visit = 'Visit',                     wake = 'wake')dip_calc(hypnos_proc)

Plot of Dipping Classifications

Description

Graphical visualization of the dip_calc output for categories of dipping percentages.

NOTE: Any reference to "sleep" in the bp package refers to an individual's nocturnal period;"sleep" is used in an informal sense for intuitive purposes. Technically, from a clinical perspective,indication of sleep is currently obtained through means of EEG or other highly specialized equipment.For all intents and purposes, sleep in the context of this package refers to actigraphy-inferrednocturnal periods of rest.

Usage

dip_class_plot(  data,  subj = NULL,  dip_thresh = 0.1,  extreme_thresh = 0.2,  thresh_mult = 2,  sleep_start_end = NULL)

Arguments

Value

A scatter plot of all dipping percentage values layered on top of the category plot outlined inOkhubo et al. (1995). dip_thresh and extreme_thresh denote the cutoffs for the Normal and Extremedipping categories. Any dips below zero are denoted as Inverted (or Reverse) dipping.

The default plot categories are as follows:

• INV: Inverted (Reverse) Dipper - no nocturnal decline (greater or equal to 0%)

• ND: Non-Dipper - a nocturnal decline between 0 - 10%

• DIP: Dipper - a nocturnal decline between 10% and the extreme dipping % (20%)

• ED: Extreme Dipper - a nocturnal decline exceeding 20%

References

Okhubo, T., Imai, Y., Tsuji, K., Nagai, K., Watanabe, N., Minami, J., Kato, J., Kikuchi, N., Nishiyama, A.,Aihara, A., Sekino, M., Satoh, H., and Hisamichi, S. (1997). Relation Between Nocturnal Decline in BloodPressure and Mortality: The Ohasama Study,American Journal of Hypertension10(11), 1201–1207,doi:10.1016/S0895-7061(97)00274-4.

Examples

hypnos_proc <- process_data(bp::bp_hypnos,                               sbp = "syst",                               dbp = "DIAST",                               date_time = "date.time",                               id = "id",                               wake = "wake",                               visit = "visit")dip_class_plot(hypnos_proc)

Blood Pressure Tables in Graphical Format

Description

Blood Pressure Tables in Graphical Format

Usage

dow_tod_plots(data, subj = NULL)

Arguments

Value

A list of four 'gtables' that correspond to the tables for Day of Week andTime of Day broken down by bothSBP andDBP.

Examples

data("bp_hypnos")hyp_proc <- process_data(bp_hypnos,                         bp_type = 'ABPM',                         sbp = "syst",                         dbp = "DIAST",                         date_time = "date.time",                         id = "id",                         wake = "wake",                         visit = "visit",                         hr = "hr",                         map = "map",                         rpp = "rpp",                         pp = "pp",                         ToD_int = c(5, 13, 18, 23))rm(bp_hypnos)dow_tod_plots_out <- dow_tod_plots(hyp_proc)grid::grid.draw(   gridExtra::grid.arrange(dow_tod_plots_out[[1]], dow_tod_plots_out[[2]], ncol = 2) )

Compatibility Check forpath Argument

Description

Compatibility Check forpath Argument

Usage

path_check(path)

Arguments

Value

A formatted string corresponding to the proper binding ofdirectory and base naming conventions. If user's path is invalid,path_check will throw an error.

Examples

## Not run: path = "~dir/abpm/"path_check(path) # drops the trailing slash## End(Not run)

Data Pre-Processor

Description

A helper function to assist in pre-processing the user-suppliedinput data in a standardized format for use with other functions in thebp package.See Vignette for further details.

Usage

process_data(  data,  bp_type = c("hbpm", "abpm", "ap"),  guidelines = c("Lee_2020", "AHA", "Custom"),  bp_cutoffs = list(c(100, 120, 130, 140, 180), c(60, 80, 80, 90, 120)),  ap = NULL,  time_elap = NULL,  sbp = NULL,  dbp = NULL,  date_time = NULL,  id = NULL,  group = NULL,  wake = NULL,  visit = NULL,  hr = NULL,  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")

Arguments

Value

A processed dataframe object with standardized column names and formats to use with the rest of bp package functions. The following standardized column names are used throughout

References

Lee H, Yano Y, Cho SMJ, Park JH, Park S, Lloyd-Jones DM, Kim HC. Cardiovascular risk of isolatedsystolic or diastolic hypertension in young adults.Circulation. 2020; 141:1778-1786.doi:10.1161/CIRCULATIONAHA.119.044838

Muntner, P., Carey, R. M., Jamerson, K., Wright Jr, J. T., & Whelton, P. K. (2019). Rationale for ambulatory and home blood pressure monitoring thresholds in the 2017 American College of Cardiology/American Heart Association Guideline. Hypertension, 73(1), 33-38.doi:10.1161/HYPERTENSIONAHA.118.11946

Omboni, S., Parati, G*., Zanchetti, A., Mancia, G. Calculation of trough: peak ratio ofantihypertensive treatment from ambulatory blood pressure: methodological aspectsJournal of Hypertension. October 1995 - Volume 13 - Issue 10 - p 1105-1112doi:10.1097/00004872-199510000-00005

Unger, T., Borghi, C., Charchar, F., Khan, N. A., Poulter, N. R., Prabhakaran, D., ... & Schutte,A. E. (2020). 2020 International Society of Hypertension global hypertension practice guidelines.Hypertension, 75(6), 1334-1357.doi:10.1161/HYPERTENSIONAHA.120.15026

Examples

# Load bp_hypnosdata("bp_hypnos")# Process data for bp_hypnoshypnos_proc <- process_data(bp_hypnos,                              bp_type = 'abpm',                              sbp = 'syst',                              dbp = 'diast',                              date_time = 'date.time',                              hr = 'hr',                              pp = 'PP',                              map = 'MaP',                              rpp = 'Rpp',                              id = 'id',                              visit = 'Visit',                              wake = 'wake',                              data_screen = FALSE)hypnos_proc# Load bp_jhs datadata("bp_jhs")# Process data for bp_jhs# Note that bp_type defaults to "hbpm" and is therefore not specifiedjhs_proc <- process_data(bp_jhs,                         sbp = "Sys.mmHg.",                         dbp = "Dias.mmHg.",                         date_time = "DateTime",                         hr = "Pulse.bpm.")jhs_proc

Sleep Interval Manual Override

Description

Adjusts WAKE column to reflect start and end of user-defined sleep period. If sleep_start_endis NULL and WAKE column is included in input data, this function will not alter data.

Usage

sleep_int(data, sleep_start_end = NULL, adj = TRUE)

Arguments

Value

Dataframe with added / adjusted WAKE column moved after DATE_TIME column containing binary 1 (awake)and 0 (asleep)

Compatibility Checks for User-Supplied SBP/DBP Stages Vector

Description

Used inprocess_data function

Usage

stage_check(sbp_stages = NULL, dbp_stages = NULL)

Arguments

Value

A list containing two vectors that each correspond to the SBP/DBP stages for use in further processing.If the function returns no error, the supplied vectors are valid. Function arguments are coerced to integer.

Compatibility Check for User-Supplied Subject Subset Vector

Description

Compatibility Check for User-Supplied Subject Subset Vector

Usage

subject_subset_check(data, subj = NULL)

Arguments

Value

Logical indicating whether the subj vector passes the checks (TRUE),or not (FALSE)

Successive Variation (SV)

Description

THIS IS A DEPRECATED FUNCTION. USE bp_sv INSTEAD.

Usage

sv(  data,  inc_date = FALSE,  subj = NULL,  bp_type = 0,  add_groups = NULL,  inc_wake = TRUE)

Arguments

Details

Calculate the successive variation (SV) at various levels of granularitybased on what is supplied (ID, VISIT, WAKE, and / or DATE)for either SBP,DBP, or both. SV is a measure of dispersion that takes into account thetemporal structure of the data and relies on the sum of squared differencesin successive observations, unlike the average real variability (ARV)which relies on the sum of absolute differences.

SV = \sqrt{\frac{\sum(x_{i+1} - x_i)^2}{n-1}}

NOTE: The canonical standard deviation, independent of the temporalstructure using the sample average, is added for comparison:

SD = \sqrt{\frac{\sum(x_{i+1} - \bar{x})^2}{n-1}}

Value

A tibble object with a row corresponding to each subject, or alternativelya row corresponding to each date if inc_date = TRUE. The resulting tibble consists of:

• ID: The unique identifier of the subject. For single-subject datasets, ID = 1

• VISIT: (If applicable) Corresponds to the visit # of the subject, if more than 1

• WAKE: (If applicable) Corresponds to the awake status of the subject (0 = asleep |1 = awake)

• SV_SBP /SV_DBP: Calculates the square root of the average squared differencesbetween successive measurements. The resulting value averages across the granularitygrouping for however many observations are present.

• N: The number of observations for that particular grouping. Ifinc_date = TRUE,N corresponds to the number of observations for that date. Ifinc_date = FALSE, Ncorresponds to the number of observations for the most granular grouping available (i.e.a combination ofID,VISIT, andWAKE)

• Any add_groups variables supplied to function argument will be present as a column in theresulting tibble.

Examples

# Load datadata(bp_hypnos)data(bp_jhs)# Process bp_hypnoshypnos_proc <- process_data(bp_hypnos, sbp = "SYST", dbp = "DIAST", date_time = "date.time",id = "id", wake = "wake", visit = "visit", hr = "hr", pp ="pp", map = "map", rpp = "rpp")# Process bp_jhs datajhs_proc <- process_data(bp_jhs, sbp = "Sys.mmHg.", dbp = "Dias.mmHg.", date_time = "DateTime",hr = "Pulse.bpm.")# SV Calculation## Not run: bp_sv(hypnos_proc)bp_sv(jhs_proc, add_groups = c("meal_time"))# Notice that meal_time is not a column from process_data, but it still works## End(Not run)