The bp Package
The general workflow of the bp package consists of 1) a data processing stage and 2) an analysis stage, in ideally as little as two lines of code. The processing stage formats the user’s supplied input data in such a way that it adheres to the rest of the bp functions. The analysis stage uses the processed data to quantify various attributes of the blood pressure relationships or to provide various visualizations. One of the key abilities of the bp package is bp_report function which generates a report that combines such visualization plots into one easily digestible summary for clinicians or researchers to interpret an individual’s (or multiple individuals’) blood pressure results. We will walk through each of these stages in the subsequent sections.
Data Processing with the process_data function
Before any analysis can be done, the user-supplied data set must be first processed into the proper format using process_data to adhere to package data structure requirements and naming conventions. This function ensures that user-supplied data columns aren’t double counted or missed, since blood pressure data are often inconsistent and come from a wide variety of formats. While a tedious initial step, it will save time in the long-run as the resulting processed data will not require any future specification, which can then be directly plugged into the analysis functions. It is worth noting that if the user-supplied data set already adheres to the column naming conventions and data types, then the process_data function will be unnecessary. However, it is good practice to still make use of this function as a sanity check to verify all available variables.
The basic workflow is to load in the user-supplied unprocessed raw data, process it with the process_data function and save to a new dataframe. Note that the capitalization does not matter when specifying the columns.
## Load the sample bp_hypnos
## In this scenario, the bp_hypnos acts as the "user-supplied" data that is to be processed
data("bp_hypnos")
## Assign the output of the process_data function to a new dataframe object
hypnos_proc <- process_data(bp_hypnos,
bp_type = "abpm",
sbp = 'syst',
dbp = 'diast',
date_time = 'date.time',
id = 'id',
visit = 'visit',
hr = 'hr',
wake = 'wake',
pp = 'pp',
map = 'map',
rpp = 'rpp')
#> 2 values that exceeded the DUL or DLL thresholds were coerced to NA.Notice how the column names of the original bp_hypnos changed in the processed data. Notably, SYST became SBP, DIAST became DBP, and DATE.TIME became DATE_TIME.
names(bp_hypnos)
#> [1] "NR." "DATE.TIME" "SYST" "MAP" "DIAST" "HR"
#> [7] "PP" "RPP" "WAKE" "ID" "VISIT" "DATE"
names(hypnos_proc)
#> [1] "ID" "GROUP" "DATE_TIME" "DATE" "DAY_OF_WEEK"
#> [6] "YEAR" "MONTH" "DAY" "HOUR" "TIME_OF_DAY"
#> [11] "SBP" "DBP" "BP_CLASS" "HR" "MAP"
#> [16] "MAP_OLD" "RPP" "PP" "PP_OLD" "WAKE"
#> [21] "VISIT" "NR." "SBP_CATEGORY" "DBP_CATEGORY" "BP_TYPE"While the results seem to be trivial at first glance, let’s see what happens when we use a much different data set with a completely different naming convention: the bp_jhs data set. Unlike bp_hypnos which has all of the available columns needed in the process_data function with multiple subjects, bp_jhs is a single-subject data set without many of the multi-subject identifiers such as ID, VISIT, or WAKE (as it is non-ABPM data). Further, there is no MAP or PP column, but these (as we will see) can be automatically created.
## Load the sample bp_jhs data set
## As before, this is what will be referred to as the "user-supplied" data set
data("bp_jhs")
## Assign the output of the process_data function to a new dataframe object
jhs_proc <- process_data(bp_jhs,
sbp = 'sys.mmhg.',
dbp = 'dias.mmhg.',
date_time = 'datetime',
hr = 'PULSE.BPM.')
#> DATE column created from DATE_TIME column.
#> No PP column found or specified. Automatically generated from SBP and DBP columns.
#> No RPP column found or specified. Automatically generated from SBP and HR columns.
#> No MAP column found or specified. Automatically generated from SBP and DBP columns.
head(jhs_proc, 5)
#> ID GROUP DATE_TIME DATE DAY_OF_WEEK YEAR MONTH DAY HOUR
#> 1 1 1 2019-08-01 09:15:54 2019-08-01 Thu 2019 8 1 9
#> 2 1 1 2019-07-31 11:39:59 2019-07-31 Wed 2019 7 31 11
#> 3 1 1 2019-07-31 11:38:07 2019-07-31 Wed 2019 7 31 11
#> 4 1 1 2019-07-30 13:47:46 2019-07-30 Tue 2019 7 30 13
#> 5 1 1 2019-07-30 13:46:15 2019-07-30 Tue 2019 7 30 13
#> TIME_OF_DAY SBP DBP BP_CLASS HR MAP RPP PP DAYOFWK MEAL_TIME BPDELTA
#> 1 Morning 132 80 Stage 1 79 97.33333 10428 52 Thu Breakfast 52
#> 2 Morning 126 77 Elevated 62 93.33333 7812 49 Wed Breakfast 49
#> 3 Morning 128 76 Elevated 60 93.33333 7680 52 Wed Breakfast 52
#> 4 Afternoon 130 81 Stage 1 63 97.33333 8190 49 Tue Lunch 49
#> 5 Afternoon 134 83 Stage 1 62 100.00000 8308 51 Tue Lunch 51
#> SBP_CATEGORY DBP_CATEGORY BP_TYPE
#> 1 Stage 1 Stage 1 HBPM
#> 2 Elevated Normal HBPM
#> 3 Elevated Normal HBPM
#> 4 Stage 1 Stage 1 HBPM
#> 5 Stage 1 Stage 1 HBPMAfter a quick inspection of the original bp_jhs data set and the newly processed data data set, it should be evident that there was a lot going on “under the hood” of the process_data function. As we can see from the column names, the awkward nuisance of typing sys.mmhg., dias.mmhg., pulse.bpm., and datetime have now been replaced with the more concise SBP, DBP, HR, and DATE_TIME names, respectively. Additionally, MAP, PP, RPP, SBP_Category, and DBP_Category were all calculated as additional columns which previously did not exist in the data. Additionally, if the supplied data has a column corresponding to a “date/time” format, the columns Time_of_Day and DAY_OF_WEEK will also be created for ease.
names(bp_jhs)
#> [1] "DateTime" "Month" "Day" "Year" "DayofWk"
#> [6] "Hour" "Meal_Time" "Sys.mmHg." "Dias.mmHg." "bpDelta"
#> [11] "Pulse.bpm."
names(jhs_proc)
#> [1] "ID" "GROUP" "DATE_TIME" "DATE" "DAY_OF_WEEK"
#> [6] "YEAR" "MONTH" "DAY" "HOUR" "TIME_OF_DAY"
#> [11] "SBP" "DBP" "BP_CLASS" "HR" "MAP"
#> [16] "RPP" "PP" "DAYOFWK" "MEAL_TIME" "BPDELTA"
#> [21] "SBP_CATEGORY" "DBP_CATEGORY" "BP_TYPE"NOTE: For consistency, process_data will coerce all column names to upper-case.
Blood Pressure Metrics
After the data has been processed, we can now utilize the built-in metrics from the literature to characterize the blood pressure variability. To start, the following metrics are what is currently offered through the bp package:
| Function | Metric Name | Source |
|---|---|---|
bp_arv |
Average Real Variability | Mena et al (2005) |
bp_center |
Mean and Median | Amaro Lijarcio et al (2006) |
bp_cv |
Coefficient of Variation | Munter et al (2011) |
bp_mag |
Blood Pressure Magnitude (peak and trough) | Munter et al (2011) |
bp_range |
Blood Pressure Range | Levitan et al (2013) |
bp_sv |
Successive Variation | Munter et al (2011) |
bp_sleep_metrics |
Blood Pressure Sleep Metrics | (Multiple - see documentation) |
bp_stages |
Blood Pressure Stages Classification | American Heart Association |
bp_stats |
Aggregation of statistical summaries | N/A |
dip_calc |
Nocturnal Dipping % and Classification | Okhubo et al (1997) |
Time-Dependent Dispersion Metrics
bp_arv- Average Real Variability- A measure of dispersion using the sum of absolute differences in successive observations
bp_sv- Successive Variation- A measure of dispersion using the sum of squared differences in successive observations
Time-Independent Dispersion Metrics
bp_mag- Blood Pressure Magnitude (peak and trough)- Peak measures the distance from the average value to the maximum value
- Trough measures the distance from the minimum value to the average value
bp_range- Blood Pressure Range- Range measures the distance from the minimum value to the maximum value
bp_cv- Coefficient of Variation- Coefficient of Variation is a ratio of the standard deviation / average
Sleep-dependent Metrics
dip_calc- Nocturnal Dipping % and Classification- Nocturnal dipping percentage is the % drop in blood pressure while asleep compared with awake. Requires an indication of when a subject is asleep to know how to calculate:
1 - (avg sleep BP / avg daytime BP). The severity of the dipping percentage indicates the corresponding classification of that individual (dipper, non-dipper, reverse dipper).
- Nocturnal dipping percentage is the % drop in blood pressure while asleep compared with awake. Requires an indication of when a subject is asleep to know how to calculate:
bp_sleep_metrics- Sleep-specific metrics for BP- This function includes the following sleep metrics: dipping calculation, nocturnal fall, morning blood pressure surge (MBPS) calculated both sleep-trough and pre-wake variants, morningness-eveningness average and difference, and weighted standard deviation (wSD)
Let’s say we are working with the bp_hypnos and would like to compare the time-dependent nature of the bp_arv with the bp_sv for each subject.
head(bp_arv(hypnos_proc, bp_type = 'both'))
#> Missing SBP and/or DBP values found in data set. Removing for calculation.
#> # A tibble: 6 x 6
#> # Groups: ID, VISIT [3]
#> ID VISIT WAKE ARV_SBP ARV_DBP N
#> <fct> <fct> <fct> <dbl> <dbl> <int>
#> 1 70417 1 0 14.2 10.6 10
#> 2 70417 1 1 9.58 5.47 20
#> 3 70417 2 0 17.7 7.57 8
#> 4 70417 2 1 11.2 8.12 17
#> 5 70422 1 0 10.5 4 5
#> 6 70422 1 1 14.9 6.62 17
head(bp_sv(hypnos_proc, bp_type = 'both'))
#> Missing SBP and/or DBP values found in data set. Removing for calculation.
#> # A tibble: 6 x 6
#> # Groups: ID, VISIT [3]
#> ID VISIT WAKE SV_SBP SV_DBP N
#> <fct> <fct> <fct> <dbl> <dbl> <int>
#> 1 70417 1 0 15.8 13 10
#> 2 70417 1 1 11.2 7.50 20
#> 3 70417 2 0 18.9 8.22 8
#> 4 70417 2 1 13.0 10.2 17
#> 5 70422 1 0 11.2 5.74 5
#> 6 70422 1 1 19.1 8.80 17Comparing vertically can be challenging, so with some help from dplyr we can obtain the following:
head(dplyr::left_join(bp_arv(hypnos_proc, bp_type = 'both'), bp_cv(hypnos_proc, bp_type = 'both')))
#> Missing SBP and/or DBP values found in data set. Removing for calculation.
#> Missing SBP and/or DBP values found in data set. Removing for calculation.
#> Joining, by = c("ID", "VISIT", "WAKE", "N")
#> # A tibble: 6 x 10
#> # Groups: ID, VISIT [3]
#> ID VISIT WAKE ARV_SBP ARV_DBP N CV_SBP CV_DBP SD_SBP SD_DBP
#> <fct> <fct> <fct> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 70417 1 0 14.2 10.6 10 9.54 16.3 11.8 9.86
#> 2 70417 1 1 9.58 5.47 20 6.63 7.65 8.49 5.09
#> 3 70417 2 0 17.7 7.57 8 9.46 12.3 12.9 7.43
#> 4 70417 2 1 11.2 8.12 17 8.44 11.5 11.5 7.56
#> 5 70422 1 0 10.5 4 5 5.21 7.02 7.22 4.09
#> 6 70422 1 1 14.9 6.62 17 9.86 11.5 14.9 7.59Note that this is possible thanks to the work we did in standardizing column names from the processing step.
Because the hypnos_proc data contains BP readings during sleep, sleep-specific metrics through the bp_sleep_metrics function can be used to garner further insight:
bp_sleep_metrics(hypnos_proc)
#> $Sleep_Counts
#> # A tibble: 10 x 8
#> ID VISIT GROUP N_total N_wake N_sleep HRS_wake HRS_sleep
#> <fct> <fct> <fct> <int> <int> <int> <int> <int>
#> 1 70417 1 1 30 20 10 17 8
#> 2 70417 2 1 25 17 8 16 8
#> 3 70422 1 1 22 17 5 16 5
#> 4 70422 2 1 21 14 7 11 7
#> 5 70424 1 1 26 20 6 16 5
#> 6 70424 2 1 23 17 6 16 6
#> 7 70435 1 1 29 23 6 17 6
#> 8 70435 2 1 29 20 9 16 7
#> 9 70439 1 1 22 14 8 13 8
#> 10 70439 2 1 23 17 6 16 6
#>
#> $SBP_Sleep_Summary
#> # A tibble: 10 x 11
#> # Groups: ID, VISIT [10]
#> ID VISIT GROUP sleep_SBP wake_SBP sleep_SBP_sd wake_SBP_sd presleep_SBP
#> <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 70417 1 1 123. 128 11.8 8.49 132.
#> 2 70417 2 1 136. 136. 12.9 11.5 140
#> 3 70422 1 1 139. 151. 7.22 14.9 148
#> 4 70422 2 1 125. 152. 13.5 16.7 132.
#> 5 70424 1 1 110. 128. 14.2 13.3 128.
#> 6 70424 2 1 113 123. 8.65 15.6 132
#> 7 70435 1 1 106. 129. 10.7 10.2 145
#> 8 70435 2 1 136. 123. 9.55 14.7 144.
#> 9 70439 1 1 167 160. 7.09 15.1 159
#> 10 70439 2 1 149. 144. 15.7 9.17 138
#> # ... with 3 more variables: prewake_SBP <dbl>, postwake_SBP <dbl>,
#> # lowest_SBP <dbl>
#>
#> $DBP_Sleep_Summary
#> # A tibble: 10 x 11
#> # Groups: ID, VISIT [10]
#> ID VISIT GROUP sleep_DBP wake_DBP sleep_DBP_sd wake_DBP_sd presleep_DBP
#> <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 70417 1 1 60.5 66.6 9.86 5.09 69
#> 2 70417 2 1 60.5 65.6 7.43 7.56 74.5
#> 3 70422 1 1 58.2 65.9 4.09 7.59 70.5
#> 4 70422 2 1 60.6 69.5 6.40 7.75 64
#> 5 70424 1 1 54.6 67.8 7.99 8.31 55
#> 6 70424 2 1 55 61.5 8 6.98 69
#> 7 70435 1 1 63 82.1 6.42 6.55 92
#> 8 70435 2 1 79.2 72.5 8.61 8.13 79.5
#> 9 70439 1 1 62.6 69.3 4.93 19.9 63
#> 10 70439 2 1 60.8 56.8 3.31 3.91 56
#> # ... with 3 more variables: prewake_DBP <dbl>, postwake_DBP <dbl>,
#> # lowest_DBP <dbl>
#>
#> $Sleep_Metrics
#> # A tibble: 10 x 17
#> # Groups: ID, VISIT, GROUP [10]
#> ID VISIT GROUP dip_calc_SBP noct_fall_SBP ST_mbps_SBP PW_mbps_SBP
#> <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 70417 1 1 0.0359 1.16 2.67 -3
#> 2 70417 2 1 -0.00450 1.09 -9 -22.5
#> 3 70422 1 1 0.0819 1.09 4.17 5.5
#> 4 70422 2 1 0.174 1.05 35.7 33.7
#> 5 70424 1 1 0.141 1.24 1.33 1.33
#> 6 70424 2 1 0.0848 1.21 11.7 7
#> 7 70435 1 1 0.179 1.37 11 11.5
#> 8 70435 2 1 -0.104 1.06 -4.5 -8.90
#> 9 70439 1 1 -0.0442 0.995 19.2 9
#> 10 70439 2 1 -0.0329 0.950 8.67 -9
#> # ... with 10 more variables: ME_SBP_avg <dbl>, ME_SBP_diff <dbl>,
#> # wSD_SBP <dbl>, dip_calc_DBP <dbl>, noct_fall_DBP <dbl>, ST_mbps_DBP <dbl>,
#> # PW_mbps_DBP <dbl>, ME_DBP_avg <dbl>, ME_DBP_diff <dbl>, wSD_DBP <dbl>Turning back to the bp_jhs data set, let’s examine the peaks and troughs of the BP readings. We would call the bp_mag function on our data.
head(bp_mag(jhs_proc, bp_type = 'both'))
#> # A tibble: 1 x 6
#> ID Peak_SBP Peak_DBP Trough_SBP Trough_DBP N
#> <fct> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 1 20.3 15.1 19.7 17.9 222Here, we notice something different. Because there weren’t ID, VISIT, or WAKE columns, the bp_mag aggregated everything together. This is technically correct, but say we wanted to glean more information by breaking our data down by DATE instead; we would need to include the inc_date = TRUE optional argument to the function.
tail(bp_mag(jhs_proc, inc_date = TRUE, bp_type = 'both'))
#> # A tibble: 6 x 7
#> # Groups: ID [1]
#> ID DATE Peak_SBP Peak_DBP Trough_SBP Trough_DBP N
#> <fct> <date> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 1 2019-07-26 8.25 3.5 6.75 2.5 4
#> 2 1 2019-07-28 5.5 3 4.5 3 4
#> 3 1 2019-07-29 7 2.75 9 2.25 4
#> 4 1 2019-07-30 2 1 2 1 2
#> 5 1 2019-07-31 1 0.5 1 0.5 2
#> 6 1 2019-08-01 0 0 0 0 1Interpretation: While it may not seem obvious at first glance, the blood pressure magnitude (whether a peak or a trough) is calculated as \(peak = max(BP) - mean(BP)\) and \(trough = mean(BP) - min(BP)\) where BP could correspond to either SBP or DBP. If we manually inspect the data from 2019-07-31 we see that N = 2 measurements and within the bp_jhs data set we see the two measurements are 126 and 128 for SBP and 77, and 76 for DBP. \(\bar{x}_{SBP} = \frac{(126+128)}{2} = 127\) and \(\bar{x}_{DBP} = \frac{(78+76)}{2} = 76.5\) so the respective peak and trough values from our output make sense.
Visualization
So far, we have processed the original data and ran a couple metrics to get a clearer picture of the variability, now let’s visualize it. Though the processed data can easily be incorporated into other visualization packages or code (such as ggplot which we will demonstrate in the first example below with bp_mag), the following visuals are currently included with the bp package:
| Function | Visual |
|---|---|
bp_scatter |
Scatter plot of BP stages (American Heart Association) |
bp_ts_plots |
Time series plots |
bp_hist |
Histograms of BP stages |
dip_class_plot |
Dipping % category plot |
bp_report |
Exportable report of BP summary |
dow_tod_plots |
Day of Week / Time of Day chart |
The bp_hist returns three histograms of all readings corresponding to total number within each stage, frequency of SBP readings, and frequency of DBP readings. Furthermore, it breaks the data down by color according to which blood pressure stage it falls under:
Let’s now suppose that we wish to break down our readings by Time of Day and Day of Week. For this, we can implement the dow_tod_plots function. Because this function is mainly used as a helper function for the bp_report function, we need to add a couple steps using the gridExtra package
bptable_ex <- dow_tod_plots(jhs_proc)
#gridExtra::grid.arrange(bptable_ex[[1]], bptable_ex[[2]], bptable_ex[[3]], bptable_ex[[4]], nrow = 2)As a final step before returning to our other example, let’s compile everything that we have done so far into a more compact and digestible report that visualizes everything simultaneously. To do so, we will rely on the bp_report function, which will generate a report in PDF (although other formats such as PNG are available):
Suppose we wanted to look at the observations for the bp_jhs data set over time. The time series plotting capabilities allow this to be visualized. The output returns two plots, one for the total duration through time, and another showing repeated measurements within a 24 hour period (by hour). In the event that there are more than one series of recordings (such as the bp_hypnos data set where there are two visits per subject), the wrap_var and group_var function arguments can be employed.
Suppose now that we turn our attention back to the bp_hypnos example, suppose now we wish to examine sleep patterns. Using the dipping category plot we can analyze BP behavior during sleep
