R/bp_tables.R
In johnschwenck/bp: Blood Pressure Analysis in R
Defines functions
bp_tables
Documented in
bp_tables
#' Blood Pressure Tables
#'
#' @description Generate a list of pertinent table outputs that detail various information
#' specific to blood pressure.
#'
#' @param data A processed dataframe resulting from the \code{process_data} function that
#' contains the \code{SBP}, \code{DBP}, \code{DAY_OF_WEEK}, \code{Time_of_Day}, \code{SBP_CATEGORY},
#' and \code{DBP_CATEGORY} columns.
#'
#' @param bp_type Optional argument. Determines whether to calculate tables for SBP
#' values or DBP values, or both. For \strong{both SBP and DBP} ARV values use bp_type = 'both',
#' for \strong{SBP-only} use bp_type = 'sbp, and for \strong{DBP-only} use bp_type = 'dbp'.
#' If no type specified, default will be set to 'both'
#'
#' @param bp_perc_margin An optional argument that determines which of the marginal totals
#' to include in the raw count tables expressed as percentages. The argument can take on
#' values either NULL (default, both SBP and DBP), 1 (SBP only), or 2 (DBP only).
#'
#' @param wake_perc_margin An optional argument that determines which of the marginal totals
#' to include in the tables pertaining to the percentages of awake / asleep readings if
#' applicable (i.e. if the WAKE column is present). The argument can take on values either
#' NULL (both SBP and DBP), 1 (SBP only), or 2 (DBP only).
#'
#' @param subj Optional argument. Allows the user to specify and subset specific subjects
#' from the \code{ID} column of the supplied data set. The \code{subj} argument can be a single
#' value or a vector of elements. The input type should be character, but the function will
#' comply with integers so long as they are all present in the \code{ID} column of the data.
#'
#'
#' @return A list of table outputs for various subsets of the data based on which bp_type is selected
#'
#' @export
#'
#' @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)
#'
bp_tables <- function(data, bp_type = c('both', 'sbp', 'dbp'), bp_perc_margin = NULL, wake_perc_margin = 2, subj = NULL){
SBP_CATEGORY = DBP_CATEGORY = BP_CLASS = ID = Perc = NULL
rm(list = c('SBP_CATEGORY', 'DBP_CATEGORY', 'BP_CLASS', 'ID', 'Perc'))
# Check that bp_type, bp_perc_margin and wake_perc_margin have acceptable input
###############################################################################3
# Match argument for bp_type
bp_type <- tolower(bp_type)
bp_type <- match.arg(bp_type)
# Check bp_perc_margin argument
if( !is.null(bp_perc_margin) ){
if( !(bp_perc_margin %in% c(1, 2)) ){
stop('bp_perc_margin can only take on either NULL (overall (no marginal) percentages), 1 (row margin percentages), or 2 (column margin percentages).')
}
}
if( !is.null(wake_perc_margin) ){
if(!(wake_perc_margin %in% c(NULL, 1, 2)) ){
stop('wake_perc_margin can only take on either NULL (overall (no marginal) percentages), 1 (row margin percentages), or 2 (column margin percentages).')
}
}
# Check if user supplies a vector corresponding to a subset of multiple subjects (multi-subject only)
######################################################################################
if(!is.null(subj)){
# check to ensure that supplied subject vector is compatible
subject_subset_check(data, subj)
if(length(unique(data$ID)) > 1){
# Filter data based on subset of subjects
data <- data %>%
dplyr::filter(ID %in% subj)
}
}
# Create the following tables and report them as a list:
# 1) Count of the number of recordings in each stage
# 2) Contingency table of SBP vs DBP
# 3) Contingency table of each bp type (SBP / DBP) and Weekday
# 4) Contingency table of each bp type (SBP / DBP) and Time of Day
# Number of Recordings in each BP stage and their respective percentages
stages_SBP <- data %>% dplyr::count(SBP_CATEGORY) %>% dplyr::mutate(Perc = prop.table((data %>% dplyr::count(SBP_CATEGORY))$n), type = "SBP")
stages_DBP <- data %>% dplyr::count(DBP_CATEGORY) %>% dplyr::mutate(Perc = prop.table((data %>% dplyr::count(DBP_CATEGORY))$n), type = "DBP")
stages_CLASS <- data %>% dplyr::count(BP_CLASS) %>% dplyr::mutate(Perc = prop.table((data %>% dplyr::count(BP_CLASS))$n), type = "CLASS")
# Rename the first column of each data frame to the same Category
names(stages_SBP)[1] <- "Category"
names(stages_DBP)[1] <- "Category"
names(stages_CLASS)[1] <- "Category"
# Reorder columns so that first is type (SBP, DBP or class), then Category, then n and then Perc
stages_SBP <- stages_SBP[,c(4,1,2,3)]
stages_DBP <- stages_DBP[,c(4,1,2,3)]
stages_CLASS <- stages_CLASS[,c(4,1,2,3)]
# Combine all three in one data frame row-wise
stages_all <- rbind(stages_SBP, stages_DBP, stages_CLASS) # may be able to comment this line out
# Counts for each combination of SBP and DBP for each stage
stages_combo <- data %>% dplyr::count(SBP_CATEGORY, DBP_CATEGORY)
# Contingency table of SBP vs DBP (Counts)
bp_count <- stats::xtabs(~ SBP_CATEGORY + DBP_CATEGORY, data = data)
# Alternate: Contingency table of SBP vs DBP (Percentages)
bp_perc <- prop.table(bp_count, margin = bp_perc_margin)
# Add marginal sums to count table
bp_count <- as.data.frame.matrix( stats::addmargins( bp_count ) ) # Add marginal sums
if( ("DAY_OF_WEEK" %in% names(data)) == TRUE ){
# Day of Week
SBP_DoW <- as.data.frame.matrix( stats::addmargins( stats::xtabs(~ SBP_CATEGORY + DAY_OF_WEEK, data = data), margin = 2 ) )
names(SBP_DoW)[ length(names(SBP_DoW)) ] <- "Total"
DBP_DoW <- as.data.frame.matrix( stats::addmargins( stats::xtabs(~ DBP_CATEGORY + DAY_OF_WEEK, data = data), margin = 2 ) )
names(DBP_DoW)[ length(names(DBP_DoW)) ] <- "Total"
CLASS_DoW <- as.data.frame.matrix( stats::addmargins( stats::xtabs(~ BP_CLASS + DAY_OF_WEEK, data = data), margin = 2 ) )
names(CLASS_DoW)[ length(names(CLASS_DoW)) ] <- "Total"
}else{
SBP_DoW <- "N/A - DAY_OF_WEEK column not available"
DBP_DoW <- "N/A - DAY_OF_WEEK column not available"
CLASS_DoW <- "N/A - DAY_OF_WEEK column not available"
}
if( ("TIME_OF_DAY" %in% names(data)) == TRUE ){
# Time of Day
SBP_ToD <- as.data.frame.matrix( stats::addmargins( stats::xtabs(~ SBP_CATEGORY + TIME_OF_DAY, data = data), margin = 2 ) )
DBP_ToD <- as.data.frame.matrix( stats::addmargins( stats::xtabs(~ DBP_CATEGORY + TIME_OF_DAY, data = data), margin = 2 ) )
CLASS_ToD <- as.data.frame.matrix( stats::addmargins( stats::xtabs(~ BP_CLASS + TIME_OF_DAY, data = data), margin = 2 ) )
SBP_ToD <- SBP_ToD[,c(3,1,2,4,5)]
DBP_ToD <- DBP_ToD[,c(3,1,2,4,5)]
CLASS_ToD <- CLASS_ToD[,c(3,1,2,4,5)]
names(SBP_ToD)[ length(names(SBP_ToD)) ] <- "Total"
names(DBP_ToD)[ length(names(DBP_ToD)) ] <- "Total"
names(CLASS_ToD)[ length(names(CLASS_ToD)) ] <- "Total"
}else{
SBP_ToD <- "N/A - TIME_OF_DAY column not available"
DBP_ToD <- "N/A - TIME_OF_DAY column not available"
CLASS_ToD <- "N/A - TIME_OF_DAY column not available"
}
# Awake Status (if applicable)
if("WAKE" %in% names(data)){
SBP_wake <- stats::xtabs(~ SBP_CATEGORY + WAKE, data = data)
DBP_wake <- stats::xtabs(~ DBP_CATEGORY + WAKE, data = data)
SBP_wake_perc <- round(prop.table(SBP_wake, margin = wake_perc_margin),3)
DBP_wake_perc <- round(prop.table(DBP_wake, margin = wake_perc_margin),3)
SBP_wake <- as.data.frame.matrix( SBP_wake )
DBP_wake <- as.data.frame.matrix( DBP_wake )
# Consider for the future?
# xtabs(~ SBP_CATEGORY + WAKE + TIME_OF_DAY, data = data)
# xtabs(~ DBP_CATEGORY + WAKE + TIME_OF_DAY, data = data)
#
# xtabs(~ SBP_CATEGORY + WAKE + DAY_OF_WEEK, data = data)
# xtabs(~ DBP_CATEGORY + WAKE + DAY_OF_WEEK, data = data)
}else{
SBP_wake <- "N/A - WAKE column not available"
DBP_wake <- "N/A - WAKE column not available"
SBP_wake_perc <- "N/A - WAKE column not available"
DBP_wake_perc <- "N/A - WAKE column not available"
}
# Create list of tables to return
if(bp_type == 'both'){
# Both SBP and DBP tables
bp_tables_list = list(
'SBP_Counts_by_Stage' = stages_SBP,
'DBP_Counts_by_Stage' = stages_DBP,
'CLASS_Counts' = stages_CLASS,
'All_BP_Stage_Combinations' = stages_combo,
'BP_contingency_count' = bp_count,
'BP_contingency_percent' = bp_perc,
'SBP_by_Day_of_Week' = SBP_DoW,
'DBP_by_Day_of_Week' = DBP_DoW,
'CLASS_Day_of_Week' = CLASS_DoW,
'SBP_by_Time_of_Day' = SBP_ToD,
'DBP_by_Time_of_Day' = DBP_ToD,
'CLASS_Time_of_Day' = CLASS_ToD,
'SBP_by_WAKE_status' = SBP_wake,
'DBP_by_WAKE_status' = DBP_wake,
'SBP_by_WAKE_perc' = SBP_wake_perc,
'DBP_by_WAKE_perc' = DBP_wake_perc
)
}else if(bp_type == 'sbp'){
# SBP tables only
bp_tables_list = list(
'SBP_Counts_by_Stage' = stages_SBP,
'CLASS_Counts' = stages_CLASS,
'All_BP_Stage_Combinations' = stages_combo,
'BP_contingency_count' = bp_count,
'BP_contingency_percent' = bp_perc,
'SBP_by_Day_of_Week' = SBP_DoW,
'CLASS_Day_of_Week' = CLASS_DoW,
'SBP_by_Time_of_Day' = SBP_ToD,
'CLASS_Time_of_Day' = CLASS_ToD,
'SBP_by_WAKE_status' = SBP_wake,
'SBP_by_WAKE_perc' = SBP_wake_perc
)
}else if(bp_type == 'dbp'){
# DBP tables only
bp_tables_list = list(
'DBP_Counts_by_Stage' = stages_DBP,
'CLASS_Counts' = stages_CLASS,
'All_SBP_DBP_Combinations' = stages_combo,
'BP_contingency_count' = bp_count,
'BP_contingency_percent' = bp_perc,
'DBP_by_Day_of_Week' = DBP_DoW,
'CLASS_Day_of_Week' = CLASS_DoW,
'DBP_by_Time_of_Day' = DBP_ToD,
'CLASS_Time_of_Day' = CLASS_ToD,
'DBP_by_WAKE_status' = DBP_wake,
'DBP_by_WAKE_perc' = DBP_wake_perc
)
}
return(bp_tables_list)
}
johnschwenck/bp documentation built on Aug. 18, 2022, 11:30 a.m.
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Defines functions bp_tables
Documented in bp_tables
#' Blood Pressure Tables
#'
#' @description Generate a list of pertinent table outputs that detail various information
#' specific to blood pressure.
#'
#' @param data A processed dataframe resulting from the \code{process_data} function that
#' contains the \code{SBP}, \code{DBP}, \code{DAY_OF_WEEK}, \code{Time_of_Day}, \code{SBP_CATEGORY},
#' and \code{DBP_CATEGORY} columns.
#'
#' @param bp_type Optional argument. Determines whether to calculate tables for SBP
#' values or DBP values, or both. For \strong{both SBP and DBP} ARV values use bp_type = 'both',
#' for \strong{SBP-only} use bp_type = 'sbp, and for \strong{DBP-only} use bp_type = 'dbp'.
#' If no type specified, default will be set to 'both'
#'
#' @param bp_perc_margin An optional argument that determines which of the marginal totals
#' to include in the raw count tables expressed as percentages. The argument can take on
#' values either NULL (default, both SBP and DBP), 1 (SBP only), or 2 (DBP only).
#'
#' @param wake_perc_margin An optional argument that determines which of the marginal totals
#' to include in the tables pertaining to the percentages of awake / asleep readings if
#' applicable (i.e. if the WAKE column is present). The argument can take on values either
#' NULL (both SBP and DBP), 1 (SBP only), or 2 (DBP only).
#'
#' @param subj Optional argument. Allows the user to specify and subset specific subjects
#' from the \code{ID} column of the supplied data set. The \code{subj} argument can be a single
#' value or a vector of elements. The input type should be character, but the function will
#' comply with integers so long as they are all present in the \code{ID} column of the data.
#'
#'
#' @return A list of table outputs for various subsets of the data based on which bp_type is selected
#'
#' @export
#'
#' @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)
#'
bp_tables <- function(data, bp_type = c('both', 'sbp', 'dbp'), bp_perc_margin = NULL, wake_perc_margin = 2, subj = NULL){
SBP_CATEGORY = DBP_CATEGORY = BP_CLASS = ID = Perc = NULL
rm(list = c('SBP_CATEGORY', 'DBP_CATEGORY', 'BP_CLASS', 'ID', 'Perc'))
# Check that bp_type, bp_perc_margin and wake_perc_margin have acceptable input
###############################################################################3
# Match argument for bp_type
bp_type <- tolower(bp_type)
bp_type <- match.arg(bp_type)
# Check bp_perc_margin argument
if( !is.null(bp_perc_margin) ){
if( !(bp_perc_margin %in% c(1, 2)) ){
stop('bp_perc_margin can only take on either NULL (overall (no marginal) percentages), 1 (row margin percentages), or 2 (column margin percentages).')
}
}
if( !is.null(wake_perc_margin) ){
if(!(wake_perc_margin %in% c(NULL, 1, 2)) ){
stop('wake_perc_margin can only take on either NULL (overall (no marginal) percentages), 1 (row margin percentages), or 2 (column margin percentages).')
}
}
# Check if user supplies a vector corresponding to a subset of multiple subjects (multi-subject only)
######################################################################################
if(!is.null(subj)){
# check to ensure that supplied subject vector is compatible
subject_subset_check(data, subj)
if(length(unique(data$ID)) > 1){
# Filter data based on subset of subjects
data <- data %>%
dplyr::filter(ID %in% subj)
}
}
# Create the following tables and report them as a list:
# 1) Count of the number of recordings in each stage
# 2) Contingency table of SBP vs DBP
# 3) Contingency table of each bp type (SBP / DBP) and Weekday
# 4) Contingency table of each bp type (SBP / DBP) and Time of Day
# Number of Recordings in each BP stage and their respective percentages
stages_SBP <- data %>% dplyr::count(SBP_CATEGORY) %>% dplyr::mutate(Perc = prop.table((data %>% dplyr::count(SBP_CATEGORY))$n), type = "SBP")
stages_DBP <- data %>% dplyr::count(DBP_CATEGORY) %>% dplyr::mutate(Perc = prop.table((data %>% dplyr::count(DBP_CATEGORY))$n), type = "DBP")
stages_CLASS <- data %>% dplyr::count(BP_CLASS) %>% dplyr::mutate(Perc = prop.table((data %>% dplyr::count(BP_CLASS))$n), type = "CLASS")
# Rename the first column of each data frame to the same Category
names(stages_SBP)[1] <- "Category"
names(stages_DBP)[1] <- "Category"
names(stages_CLASS)[1] <- "Category"
# Reorder columns so that first is type (SBP, DBP or class), then Category, then n and then Perc
stages_SBP <- stages_SBP[,c(4,1,2,3)]
stages_DBP <- stages_DBP[,c(4,1,2,3)]
stages_CLASS <- stages_CLASS[,c(4,1,2,3)]
# Combine all three in one data frame row-wise
stages_all <- rbind(stages_SBP, stages_DBP, stages_CLASS) # may be able to comment this line out
# Counts for each combination of SBP and DBP for each stage
stages_combo <- data %>% dplyr::count(SBP_CATEGORY, DBP_CATEGORY)
# Contingency table of SBP vs DBP (Counts)
bp_count <- stats::xtabs(~ SBP_CATEGORY + DBP_CATEGORY, data = data)
# Alternate: Contingency table of SBP vs DBP (Percentages)
bp_perc <- prop.table(bp_count, margin = bp_perc_margin)
# Add marginal sums to count table
bp_count <- as.data.frame.matrix( stats::addmargins( bp_count ) ) # Add marginal sums
if( ("DAY_OF_WEEK" %in% names(data)) == TRUE ){
# Day of Week
SBP_DoW <- as.data.frame.matrix( stats::addmargins( stats::xtabs(~ SBP_CATEGORY + DAY_OF_WEEK, data = data), margin = 2 ) )
names(SBP_DoW)[ length(names(SBP_DoW)) ] <- "Total"
DBP_DoW <- as.data.frame.matrix( stats::addmargins( stats::xtabs(~ DBP_CATEGORY + DAY_OF_WEEK, data = data), margin = 2 ) )
names(DBP_DoW)[ length(names(DBP_DoW)) ] <- "Total"
CLASS_DoW <- as.data.frame.matrix( stats::addmargins( stats::xtabs(~ BP_CLASS + DAY_OF_WEEK, data = data), margin = 2 ) )
names(CLASS_DoW)[ length(names(CLASS_DoW)) ] <- "Total"
}else{
SBP_DoW <- "N/A - DAY_OF_WEEK column not available"
DBP_DoW <- "N/A - DAY_OF_WEEK column not available"
CLASS_DoW <- "N/A - DAY_OF_WEEK column not available"
}
if( ("TIME_OF_DAY" %in% names(data)) == TRUE ){
# Time of Day
SBP_ToD <- as.data.frame.matrix( stats::addmargins( stats::xtabs(~ SBP_CATEGORY + TIME_OF_DAY, data = data), margin = 2 ) )
DBP_ToD <- as.data.frame.matrix( stats::addmargins( stats::xtabs(~ DBP_CATEGORY + TIME_OF_DAY, data = data), margin = 2 ) )
CLASS_ToD <- as.data.frame.matrix( stats::addmargins( stats::xtabs(~ BP_CLASS + TIME_OF_DAY, data = data), margin = 2 ) )
SBP_ToD <- SBP_ToD[,c(3,1,2,4,5)]
DBP_ToD <- DBP_ToD[,c(3,1,2,4,5)]
CLASS_ToD <- CLASS_ToD[,c(3,1,2,4,5)]
names(SBP_ToD)[ length(names(SBP_ToD)) ] <- "Total"
names(DBP_ToD)[ length(names(DBP_ToD)) ] <- "Total"
names(CLASS_ToD)[ length(names(CLASS_ToD)) ] <- "Total"
}else{
SBP_ToD <- "N/A - TIME_OF_DAY column not available"
DBP_ToD <- "N/A - TIME_OF_DAY column not available"
CLASS_ToD <- "N/A - TIME_OF_DAY column not available"
}
# Awake Status (if applicable)
if("WAKE" %in% names(data)){
SBP_wake <- stats::xtabs(~ SBP_CATEGORY + WAKE, data = data)
DBP_wake <- stats::xtabs(~ DBP_CATEGORY + WAKE, data = data)
SBP_wake_perc <- round(prop.table(SBP_wake, margin = wake_perc_margin),3)
DBP_wake_perc <- round(prop.table(DBP_wake, margin = wake_perc_margin),3)
SBP_wake <- as.data.frame.matrix( SBP_wake )
DBP_wake <- as.data.frame.matrix( DBP_wake )
# Consider for the future?
# xtabs(~ SBP_CATEGORY + WAKE + TIME_OF_DAY, data = data)
# xtabs(~ DBP_CATEGORY + WAKE + TIME_OF_DAY, data = data)
#
# xtabs(~ SBP_CATEGORY + WAKE + DAY_OF_WEEK, data = data)
# xtabs(~ DBP_CATEGORY + WAKE + DAY_OF_WEEK, data = data)
}else{
SBP_wake <- "N/A - WAKE column not available"
DBP_wake <- "N/A - WAKE column not available"
SBP_wake_perc <- "N/A - WAKE column not available"
DBP_wake_perc <- "N/A - WAKE column not available"
}
# Create list of tables to return
if(bp_type == 'both'){
# Both SBP and DBP tables
bp_tables_list = list(
'SBP_Counts_by_Stage' = stages_SBP,
'DBP_Counts_by_Stage' = stages_DBP,
'CLASS_Counts' = stages_CLASS,
'All_BP_Stage_Combinations' = stages_combo,
'BP_contingency_count' = bp_count,
'BP_contingency_percent' = bp_perc,
'SBP_by_Day_of_Week' = SBP_DoW,
'DBP_by_Day_of_Week' = DBP_DoW,
'CLASS_Day_of_Week' = CLASS_DoW,
'SBP_by_Time_of_Day' = SBP_ToD,
'DBP_by_Time_of_Day' = DBP_ToD,
'CLASS_Time_of_Day' = CLASS_ToD,
'SBP_by_WAKE_status' = SBP_wake,
'DBP_by_WAKE_status' = DBP_wake,
'SBP_by_WAKE_perc' = SBP_wake_perc,
'DBP_by_WAKE_perc' = DBP_wake_perc
)
}else if(bp_type == 'sbp'){
# SBP tables only
bp_tables_list = list(
'SBP_Counts_by_Stage' = stages_SBP,
'CLASS_Counts' = stages_CLASS,
'All_BP_Stage_Combinations' = stages_combo,
'BP_contingency_count' = bp_count,
'BP_contingency_percent' = bp_perc,
'SBP_by_Day_of_Week' = SBP_DoW,
'CLASS_Day_of_Week' = CLASS_DoW,
'SBP_by_Time_of_Day' = SBP_ToD,
'CLASS_Time_of_Day' = CLASS_ToD,
'SBP_by_WAKE_status' = SBP_wake,
'SBP_by_WAKE_perc' = SBP_wake_perc
)
}else if(bp_type == 'dbp'){
# DBP tables only
bp_tables_list = list(
'DBP_Counts_by_Stage' = stages_DBP,
'CLASS_Counts' = stages_CLASS,
'All_SBP_DBP_Combinations' = stages_combo,
'BP_contingency_count' = bp_count,
'BP_contingency_percent' = bp_perc,
'DBP_by_Day_of_Week' = DBP_DoW,
'CLASS_Day_of_Week' = CLASS_DoW,
'DBP_by_Time_of_Day' = DBP_ToD,
'CLASS_Time_of_Day' = CLASS_ToD,
'DBP_by_WAKE_status' = DBP_wake,
'DBP_by_WAKE_perc' = DBP_wake_perc
)
}
return(bp_tables_list)
}
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