The daily costs of workaholism
Supplementary material S3: Data pre-processing
Luca Menghini, Ph.D., Cristian Balducci, Ph.D.
2024-05-17
Aims and content
The present document includes the pre-processing steps to read the
intensive longitudinal data collected with the Qualtrics platform
(Qualtrics, Seattle, WA, USA) and prepare them from the analyses. The
data were collected from a sample of full-time workers (mainly office
workers) that responded to a preliminary questionnaire
prelqs followed by a one- or two-week daily diary protocol
diary.
Here, we remove all objects from the R global environment, and we set the system time zone.
# removing all objets from the workspace
rm(list=ls())
# setting system time zone to GMT (for consistent temporal synchronization)
Sys.setenv(tz="GMT")The following R packages are used in this document (see References section):
# required packages
packages <- c("lubridate","ggplot2","gridExtra","plyr","labourR","data.table","magrittr")
# generate packages references
knitr::write_bib(c(.packages(), packages),"packagesProc.bib")
# # run to install missing packages
# xfun::pkg_attach2(packages, message = FALSE); rm(list=ls())1. Data reading
First, we read preliminary questionnaire
prelqs data exported from Qualtrics (available upon request
to the main author).
# loading preliminary questionnaire dataset (skipping first two lines including unuseful information))
prelqs <- read.csv("DATI/WHLSM2021 - prelQS.csv",
na.strings="")[3:nrow(read.csv("DATI/WHLSM2021 - prelQS.csv")),]
# removing 1 case with missing values to all items
prelqs <- prelqs[!is.na(prelqs$gender),]
# removing 1 case with no e-mail address (i.e., no identification code)
prelqs <- prelqs[!is.na(prelqs$RecipientEmail),]Second, we read the diary datasets exported from
Qualtrics as CSV files with numeric values (available upon request to
the main author). A separate diary survey (resulting in a separate
dataset) was used for afternoon _a, evening
_e, and morning _m diaries, respectively.
Moreover, a separate survey was used for Saturday morning
(_m_sat), and surveys sometimes (i.e., in case of techical
problems with the smartphones), they administered by sending anonymized
links including an e-mail field: _mail.
# loading diary datasets
diary_a <- read.csv("DATI/WEEK40/WHLSM2021_DiaryAfternoon.csv",
na.strings="")[3:nrow(read.csv("DATI/WEEK40/WHLSM2021_DiaryAfternoon.csv")),] # diary_a = Afternoon (after work)
diary_e <- read.csv("DATI/WEEK40/WHLSM2021_DiaryEvening.csv",
na.strings="")[3:nrow(read.csv("DATI/WEEK40/WHLSM2021_DiaryEvening.csv")),] # diary_e = Evening (bedtime)
diary_m <- read.csv("DATI/WEEK40/WHLSM2021_DiaryMorning.csv",
na.strings="")[3:nrow(read.csv("DATI/WEEK40/WHLSM2021_DiaryMorning.csv")),] # diary_m = Morning (before work)
diary_m_sat <- read.csv("DATI/WEEK40/WHLSM2021_DiaryMorning_sat.csv",
na.strings="")[3:nrow(read.csv("DATI/WEEK40/WHLSM2021_DiaryMorning_sat.csv")),] # diary_m_sat = Saturday Morning
# loading diary datasets with e-mail field (used in cases of technical problems)
diary_m_mail <- read.csv("DATI/WEEK40/WHLSM2021_DiaryMorning_mail.csv",
na.strings="")[3:nrow(read.csv("DATI/WEEK40/WHLSM2021_DiaryMorning_mail.csv")),]
diary_a_mail <- read.csv("DATI/WEEK40/WHLSM2021_DiaryAfternoon_mail.csv",
na.strings="")[3:nrow(read.csv("DATI/WEEK40/WHLSM2021_DiaryAfternoon_mail.csv")),]
diary_e_mail <- read.csv("DATI/WEEK40/WHLSM2021_DiaryEvening_mail.csv",
na.strings="")[3:nrow(read.csv("DATI/WEEK40/WHLSM2021_DiaryEvening_mail.csv")),]
diary_m_sat_mail <- read.csv("DATI/WEEK40/WHLSM2021_DiaryMorning_sat_mail.csv",
na.strings="")[3:nrow(read.csv("DATI/WEEK40/WHLSM2021_DiaryMorning_sat_mail.csv")),]2. Data recoding
Second, we recode the two datasets by removing unuseful columns, by
re-setting variable labels and classes, by renaming relevant columns,
and by merging the diary datasets.
2.1. Preliminary questionnaire
Here, we remove the unuseful columns, and we rename and sort the
relevant columns of the prelqs dataset.
# removing unuseful columns
prelqs[,c("Status","IPAddress","Progress","Duration..in.seconds.","Finished","RecordedDate","ResponseId",
"RecipientLastName","RecipientFirstName","LocationLatitude","LocationLongitude",
"DistributionChannel","UserLanguage","consent")] <- NULL
# renaming variables
colnames(prelqs)[1:4] <- c("start","end","ID","recruiter.ID") # participant's identifier (e-mail)
colnames(prelqs)[1:which(colnames(prelqs)=="children_1")] <- # removing question numbers from column names
gsub("_4","",gsub("_1","",colnames(prelqs)[1:which(colnames(prelqs)=="children_1")]))
colnames(prelqs)[which(substr(colnames(prelqs),1,2)=="EX")] <- paste("EX",1:7,sep="_") # recoding question numbers for EX scaleThen, we change the classes of the relevant variables by converting
dates as POSIXct (the R format for dates and times),
categorical variables as factor, and quantitative variables
as integer or numeric.
# time (POSIXct variables): start, end
prelqs$start <- as.POSIXct(prelqs$start)
prelqs$end <- as.POSIXct(prelqs$end)
# integer: age, WHLSM, WA, OC, WE, EX, WL, WFC, OWC, TS, NA
prelqs[,c(which(colnames(prelqs)=="age"),which(colnames(prelqs)=="WHLSM_1"):which(colnames(prelqs)=="NA_4"))] <-
lapply(prelqs[,c(which(colnames(prelqs)=="age"),which(colnames(prelqs)=="WHLSM_1"):which(colnames(prelqs)=="NA_4"))],as.integer)
# numeric: weekHours, weekHours_remote
colnames(prelqs)[which(substr(colnames(prelqs),1,4)=="week")] <- c("weekHours","weekHours_remote")
prelqs[,c("weight","height","weekHours","weekHours_remote")] <-
lapply(prelqs[,c("weight","height","weekHours","weekHours_remote")],as.numeric)
# factors: all other variables
prelqs[,c("recruiter.ID","gender","edu","mStatus","home","children","smoker","drugs_1","drugs_2","drugs_3",
"disfunctions_1","disfunctions_2","position","sector")] <-
lapply(prelqs[,c("recruiter.ID","gender","edu","mStatus","home","children","smoker","drugs_1","drugs_2","drugs_3",
"disfunctions_1","disfunctions_2","position","sector")],as.factor)We also recode the levels and values of sociodemographic variables as we preregistered (see preregistration here).
# gender: F, M, (Other)
prelqs$gender <- as.factor(gsub("1","M",gsub("2","F",gsub("3","Other",prelqs$gender))))
if(nlevels(prelqs$gender)==2){ cat("Recoding as M/F since nobody chose gender='Other'")
prelqs$gender <- factor(prelqs$gender,levels=c("F","M")) }## Recoding as M/F since nobody chose gender='Other'# education: highschool or lower vs. university or higher
prelqs$edu <- as.factor(gsub("1","highschool-",gsub("2","highschool-",gsub("3","university+",prelqs$edu))))
# children: yes/no
prelqs[prelqs$children!=0,"children"] <- 1
prelqs$children <- as.factor(gsub("0","No",gsub("1","Yes",prelqs$children))) # having a children
# home situation: alone, partner, children, parents, others
prelqs$home <- gsub("1","partner",gsub("2","parents",gsub("3","children",gsub("4","alone",gsub("5","others",prelqs$home)))))
prelqs$home <- factor(gsub("partner,alone","partner", # partner and alone = partner
gsub("partner,others","partner", # partner and others = partner
gsub("partner,children","children", # if children = children
gsub("alone,others","others", # alone and others = others
gsub("partner,parents","partner",prelqs$home))))), # partner and parents = partner
levels=c("alone","partner","children","parents","others"))
# children in the household: yes/no
prelqs$home_child <- as.factor(gsub("alone","No", #
gsub("partner","No",
gsub("children","Yes",
gsub("parents","No",
gsub("others","No",prelqs$home))))))
# marital status: single, partner, divorced, widowed
prelqs$mStatus <- factor(gsub("1","single",gsub("2","partner",gsub("3","divorced",gsub("4","widowed",prelqs$mStatus)))),
levels=c("single","partner","divorced","widowed"))
# having a partner
prelqs$partner <- as.factor(gsub("single","No",
gsub("partner","Yes",
gsub("divorced","No",
gsub("widowed","No",prelqs$mStatus)))))
# living with partner
prelqs$home_partner <- as.factor(gsub("alone","No", # living with partner
gsub("partner","Yes",
gsub("children","No",
gsub("parents","No",
gsub("others","No",prelqs$home))))))
# smoking status: Yes/No
prelqs$smoker <- factor(gsub("1","Yes",gsub("2","Quit_less",gsub("3","Quit_more",gsub("4","No",prelqs$smoker)))),
levels=c("No","Yes","Quit_less","Quit_more"))
prelqs$smoker <- as.factor(gsub("Quit_less","Yes",gsub("Quit_more","No",prelqs$smoker)))
# drugs/medications
colnames(prelqs)[which(substr(colnames(prelqs),1,5)=="drugs")] <- c("bp_drugs","horm_drugs","psy_drugs") # drugs/medications
prelqs$bp_drugs <- as.factor(gsub("1","Yes",gsub("2","No",prelqs$bp_drugs)))
prelqs$horm_drugs <- as.factor(gsub("1","Yes",gsub("2","No",prelqs$horm_drugs)))
prelqs$psy_drugs <- as.factor(gsub("1","Yes",gsub("2","No",prelqs$psy_drugs)))
# cardiovascular and sleep dysfunctions
colnames(prelqs)[which(substr(colnames(prelqs),1,5)=="disfu")] <- c("cv_dysf","sleep_dysf")
prelqs$cv_dysf <- as.factor(gsub("1","Yes",gsub("2","No",prelqs$cv_dysf)))
prelqs$sleep_dysf <- as.factor(gsub("1","Yes",gsub("2","No",prelqs$sleep_dysf)))
# job position: employee or project vs. manager or self-employed/employer
prelqs$position <- factor(gsub("1","Manager/Employers",
gsub("2","Employee",
gsub("3","Manager/Employers",
gsub("4","Project",
prelqs$position)))),
levels=c("Employee","Manager/Employers","Project"))
# job sector: public vs. private
prelqs$sector <- as.factor(gsub("1","Public",gsub("2","Private",prelqs$sector)))
# remotePlace (open-ended question -> making it categorical)
prelqs$remotePlace <- factor(gsub("casa","home",
gsub("acasa","casa",
gsub("dacasa","casa",
gsub("acasa/inco-wo","casa",
gsub("studioausl","studio",
gsub("ufficio","studio",
gsub("ospedale","studio",
gsub("officina","studio",
gsub("instudioinvideocall","studio",
gsub("nessuno","0",
gsub("nessuni","0",
gsub("\\.","",gsub(" ","",tolower(prelqs$remotePlace)))))))))))))),
levels=c("0","home","studio"))
# from height and weight to BMI
prelqs[prelqs$height<3,"height"] <- prelqs[prelqs$height<3,"height"]*100 # correcting heights reported in meters
prelqs$BMI <- prelqs$weight/(prelqs$height/100)^2 # BMI = kg/m^2
prelqs <- prelqs[,c(1:which(colnames(prelqs)=="age"), # removing height and weight
ncol(prelqs),which(colnames(prelqs)=="edu"):(ncol(prelqs)-1))]2.1.1. Recoding jobs
Here, the job.recode() function is used to recode the
open-ended job item responses by using the ISCO-08
classification of occupations (level 2) (Ganzeboom, 2010). The function is based on the
labouR package integrated with some manual recoding. The
open-ended responses are then removed for privacy reasons.
job.recode()
job.recode <- function(data){ require(labourR); require(data.table); require(magrittr)
# creating corpus data
corpus <- data.table(ID=data$ID,text=data$job,edu=data$edu,pos=data$position,lang="it")
languages <- unique(corpus$language) # language classes
# first screening based on the labourR::classify_occupation() function
suggestions <- classify_occupation(corpus=corpus,id_col="ID",text_col="text",lang="it",isco_level=2,num_leaves=10)
corpus <- plyr::join(corpus,suggestions,by="ID",type="left")
# adjusting automatic classification based on manual screening
corpus[corpus$text%in%c("Responsabile sistemi informativi",
"Responsabile di un piccolo museo e operatrice museale e teatrale nelle scuole"),
"preferredLabel"] <-
"Legal, social and cultural professionals"
corpus[corpus$text%in%c("Funzionario Tecnico pubblica amministrazione "),
"preferredLabel"] <-
"Legal, social and cultural professionals"
corpus[corpus$text%in%c("Banca - Responsabile Organizzazione",
"addetta amministrazione e amministratore di azienda in proprio",
"Commercialista"),
"preferredLabel"] <-
"Business and administration professionals"
corpus[corpus$text%in%c("Lavoro nella segreteria di una Scuola Secondaria di Primo Grado come amministrativa. Mi occupo di questioni burocratiche e organizzative, gestisco soprattutto circolari e domande di iscrizione, nulla osta, ecc. Al momento gestisco anche il controllo dei Green Pass e le varie procedure da seguire.",
"Attività amministrativa di ufficio e front Office ",
"impiegata commerciale",
"impiegata nel settore amministrativo",
"Addetta back office presso agenzia di somministrazione"),
"preferredLabel"] <-
"Business and administration associate professionals"
corpus[corpus$text%in%c("direzione piccola azienda",
"Dirigente d'azienda ",
"Dirigente multinazionale digital marketing",
"Founder brand moda ",
"Imprenditore nel settore degli alberghi, gestione di un hotel di 45 camere più sala ristorante. Gestione di 9 dipendenti, rapporti con fornitori e piattaforme online. Gestione della contabilità. Occasionalmente docente presso Cescot Rimini, corsi di formazione.",
"Imprenditore di carpenteria metallica ",
"Imprenditrice"),
"preferredLabel"] <-
"Chief executives, senior officials and legislators"
corpus[corpus$text%in%c("attività di ricerca presso università",
"Dottorando di Ricerca",
"Dottorato di ricerca",
"Sono un geometra contabile. Il mio lavoro consiste nel tenere traccia di tutte le lavorazioni che svolgono gli operai sui vari cantieri, dopodiché realizzo dei report fotografici e contabili per il mio datore di lavoro. \nGli operai mi inviano giornalmente le foto per realizzare i report, dalle foto ne ricavo le misure delle lavorazioni eseguite. In base alle misurazioni effettuate eseguo dei disegni contabili e realizzo un computo metrico dove controllo i costi delle lavorazioni eseguite e verifico che non superiamo il budget prefissato.",
"Artigiano idraulico elettricista",
"Geometra libero professionista"),
"preferredLabel"] <-
"Science and engineering associate professionals"
corpus[corpus$text%in%c("radiologo, analisi immagini alla workstation (computer) alternato a periodi di studio al computer e esecuzione di ecografie ",
"riabilitazione della voce e della deglutizione per adulti operati delle corde vocali",
"Psicoterapeuta presso il mio studio privato. L'attività che svolgo si rivolge ad adulti e adolescenti, quasi esclusivamente in presenza, in sedute di circa 1 h ciascuna. A ciò si aggiunge il lavoro di fatturazione, formazione e aggiornamento, gestione pratica dello studio (Back office)",
"infermiere coordinatore",
"Psichiatra psicoterapeuta "),
"preferredLabel"] <-
"Health professionals"
corpus[corpus$text%in%c("Odontotecnico da libero professionista, ",
"operatore socio sanitario",
"Tutor ABA: mi occupo dell'applicazione di procedure e strategie comportamentali per bambini con Disturbo dello spettro autistico, all'interno di un rapporto 1:1.\nDurante la sessione di lavoro è necessario costruire una buona relazione con il bambino con spettro autistico e la sua famiglia; prendere a mano note per ciascuna sessione, inserire i dati in un database; osservare il comportamento del bambino e quindi applicare una strategia idonea; monitorare gli esiti dell’intervento da condividere successivamente con il supervisore ABA, nell'ottica di una riformulazione della programmazione da implementare per quel bambino.\n"),
"preferredLabel"] <-
"Health associate professionals"
corpus[corpus$text%in%c("Coordinatore unità operativa in un servizio di integrazione socio sanitaria in salute mentale. Coordinatore unità operativa per soggetti svantaggiati e disabili nel cooperativismo. Consigliere CDA in Cooperativa. Cultore della materia in facoltà universitaria.",
"Psicologa infantile, Progettista sociale, Coordinatrice Centro Famiglie",
"Traduttrice, addetta stampa, content creator",
"project manager e consulente aziendale rispetto alla psicologia clinica, psicologia organizzazioni e psicologia emergeza"),
"preferredLabel"] <-
"Legal, social and cultural professionals"
corpus[corpus$text%in%c("Energy Back Office Specialist (Addetto al Billing Gas)",
"Solgo 2 lavori:\nLibera professione, svolgo attività di impresa nelle società con mio fratello nel campo architettura, immobiliare e turistico \nResponsabile ricerca e sviluppo (con orari flessibili) x un quartiere fieristico"),
"preferredLabel"] <-
"Science and engineering professionals"
corpus[corpus$text%in%c("Doposcuola ",
"Educatrice all'interno di una scuora superiore."),
"preferredLabel"] <-
"Teaching professionals"
corpus[corpus$text%in%c("TITOLARE DI IMPRESA DI PULIZIE"),
"preferredLabel"] <-
"Personal service workers"
corpus[corpus$text%in%c("Programmatore",
"Coach di E-Sports, alleno dei giocatori di videogiochi attraverso dei campioni professionistici"),
"preferredLabel"] <-
"Information and communications technology professionals"
# merging corpus with data
corpus$ID <- as.factor(corpus$ID)
data <- plyr::join(data,corpus[,c("ID","preferredLabel")],by="ID",type="left")
# replacing original job with recoded job categories
data$job <- as.factor(data$preferredLabel)
data$preferredLabel <- NULL # removing preferredLabel
data$ID <- as.factor(data$ID)
# summarizing info
cat("Recoded job variable into",nlevels(data$job),"categories:\n")
print(summary(data[!is.na(data$job),"job"]))
return(data) }prelqs <- job.recode(prelqs)## Recoded job variable into 22 categories:
## Administrative and commercial managers
## 2
## Assemblers
## 1
## Business and administration associate professionals
## 21
## Business and administration professionals
## 17
## Chief executives, senior officials and legislators
## 6
## Customer services clerks
## 2
## Electrical and electronic trades workers
## 1
## Handicraft and printing workers
## 1
## Health associate professionals
## 3
## Health professionals
## 9
## Hospitality, retail and other services managers
## 4
## Information and communications technology professionals
## 5
## Legal, social and cultural professionals
## 17
## Legal, social, cultural and related associate professionals
## 1
## Metal, machinery and related trades workers
## 2
## Personal service workers
## 3
## Production and specialised services managers
## 6
## Sales workers
## 2
## Science and engineering associate professionals
## 13
## Science and engineering professionals
## 14
## Stationary plant and machine operators
## 2
## Teaching professionals
## 172.1.2. recoding workTime
Then, we recode the workTime variable, which was
measured with an open-ended question as well.
# all lower case letters
workTimes <- wt.min <- wt.max <- tolower(prelqs$workTime)
# categorizing work start time
wt.min[grep("6:",wt.min)] <- # ............. # 6/7 = starting between 6:00 AM and 7:59 AM
wt.min[grep("6.",wt.min)] <-
wt.min[grep("7:",wt.min)] <-
wt.min[grep("7.",wt.min)] <- "6/7"
wt.min[grep("8:",wt.min)] <- # ............... # 8 = starting between 8:00 AM and 8:59 AM
wt.min[grep("8.",wt.min)] <- "8"
wt.min[grep("9:",wt.min)] <- # ............ # 9/10 = starting between 9:00 AM and 9:59 AM
wt.min[grep("9.",wt.min)] <-
wt.min[grep("10:",wt.min)] <-
wt.min[grep("10.",wt.min)] <- "9/10"
wt.min[grep("15-",wt.min)] <- # ............. # PM = starting in the afternoon
wt.min[grep("15.",wt.min)] <-
wt.min[grep("14.",wt.min)] <- "PM"
# categorizing work end time
wt.max[grep("19:",wt.max)] <- # ............. # 19/21 = ending between 7:00 PM and 6:59 PM
wt.max[grep("19.",wt.max)] <-
wt.max[grep("-19",wt.max)] <-
wt.max[grep("- 19",wt.max)] <-
wt.max[grep("- 20",wt.max)] <-
wt.max[grep("- 21",wt.max)] <- "19/21"
wt.max[grep("17:",wt.max)] <- # ............. # 17/18 = ending between 5:00 PM and 6:59 PM
wt.max[grep("17.",wt.max)] <-
wt.max[grep("-17",wt.max)] <-
wt.max[grep("- 17",wt.max)] <-
wt.max[grep("18:",wt.max)] <-
wt.max[grep("18.",wt.max)] <-
wt.max[grep("-18",wt.max)] <-
wt.max[grep("- 18",wt.max)] <- "17/18"
wt.max[grep("15.",wt.max)] <- #............. # 15/16 = ending between 3:00 PM and 4:59 PM
wt.max[grep("15:",wt.max)] <-
wt.max[grep("-15",wt.max)] <-
wt.max[grep("16.",wt.max)] <-
wt.max[grep("16:",wt.max)] <- "15/16"
wt.max[grep("12:",wt.max)] <- # ............. # 12/14 = ending between noon and 2:59 PM
wt.max[grep("12.",wt.max)] <-
wt.max[grep("13:",wt.max)] <-
wt.max[grep("13.",wt.max)] <-
wt.max[grep("_13",wt.max)] <-
wt.max[grep("/13",wt.max)] <-
wt.max[grep("-13",wt.max)] <-
wt.max[grep("14:",wt.max)] <-
wt.max[grep("14.",wt.max)] <-
wt.max[grep("/14",wt.max)] <-
wt.max[grep("-2.",wt.max)] <-
wt.max[grep("- 02",wt.max)] <- "12/14"
# joining start and end time
wt <- paste(wt.min,wt.max,sep="_")
wt[grep("PM_",wt)] <- "PM only" # correcting some cases and merging categories with few cases
# replacing workTime variable with recoded work times
prelqs$workTime <- as.factor(wt)
summary(prelqs$workTime)## 6/7_12/14 6/7_15/16 6/7_17/18 6/7_19/21 8_12/14 8_15/16 8_17/18
## 2 11 29 4 13 3 38
## 8_19/21 9/10_12/14 9/10_15/16 9/10_17/18 9/10_19/21 PM only
## 15 5 3 5 18 32.2. Daily diaries
Here, we merge and recode the daily diary datasets.
2.2.1. Data cleaning
Here, we filter trial responses, we remove unuseful columns, and we
rename and sort the relevant columns of the diary
datasets.
# filtering trial/preview responses
diary_m <- diary_m[diary_m$DistributionChannel=="email",]
diary_m_sat <- diary_m_sat[diary_m_sat$DistributionChannel=="email",]
diary_a <- diary_a[diary_a$DistributionChannel=="email",]
diary_e <- diary_e[diary_e$DistributionChannel=="email",]
# removing unuseful variables
toRemove <- c("Duration..in.seconds.","Status","IPAddress","Progress","Finished","RecordedDate","ResponseId",
"RecipientLastName","RecipientFirstName","LocationLatitude","LocationLongitude","DistributionChannel","UserLanguage")
diary_m[,toRemove] <- diary_a[,toRemove] <- diary_e[,toRemove] <- diary_m_mail[,toRemove] <-
diary_a_mail[,toRemove] <- diary_m_sat[,toRemove] <- diary_m_sat_mail[,toRemove] <- diary_e_mail[,toRemove] <- NULL2.2.2. Direct e-mails
Then, we merge each diary dataset with the corresponding
data obtained by directly e-mailing participants (i.e., in case of
technical/schedule problems).
# joining 'ordinary' datasets with datasets e-mailed datasets
diary_a_mail$RecipientEmail <- diary_a_mail$Q22_1 # afternoon
diary_a_mail$Q22_1 <- NULL
diary_a <- rbind(diary_a,diary_a_mail)
diary_e_mail$RecipientEmail <- diary_e_mail$e.mail_1 # evening
diary_e_mail$e.mail_1 <- NULL
diary_e <- rbind(diary_e,diary_e_mail)
diary_m_mail$RecipientEmail <- diary_m_mail$Q30_1 # morning
diary_m_mail$Q30_1 <- NULL
colnames(diary_m) <- gsub("\\.","",colnames(diary_m))
diary_m <- rbind(diary_m,diary_m_mail)
diary_m_sat_mail$RecipientEmail <- diary_m_sat_mail$Q11_1 # Saturday morning
diary_m_sat_mail$Q11_1 <- NULL
diary_m_sat_mail <- diary_m_sat_mail[!is.na(diary_m_sat_mail$RecipientEmail),] # removing trial entries
diary_m_sat <- rbind(diary_m_sat,diary_m_sat_mail)
diary_m_sat$dayOff_today <- diary_m_sat$AWL_1 <- diary_m_sat$AWL_2 <- diary_m_sat$AWL_3 <- NA # no AWL values on Saturday
diary_m <- rbind(diary_m,diary_m_sat)
# sorting and renaming columns
diary_m <- diary_m[,c(1:which(colnames(diary_m)=="ExternalReference"),which(colnames(diary_m)=="dayOff_today"),
which(colnames(diary_m)=="BP1_1"):which(colnames(diary_m)=="dayOff_yesterday"),
which(colnames(diary_m)=="AWL_1"):ncol(diary_m))]
colnames(diary_m)[1:9] <- colnames(diary_a)[1:9] <- colnames(diary_e)[1:9] <- c("start","end","ID","recruiter.ID","dayOff_today",
"SBP1","DBP1","SBP2","DBP2")
colnames(diary_a)[5] <- colnames(diary_e)[5] <- "dayOff" # differentiating afternoon/evening from morning dayOff values
colnames(diary_a) <- gsub("WHLSM1","WHLSM",gsub("WHLSM2","WHLSM",colnames(diary_a)))2.2.3. Merging diary data
Here, we merge the four datasets into a single long-form
diary dataset. The diaryType variable is
created for discriminating among Morning,
Afternoon, and Evening diaries.
# adding diaryType column
diary_m$diaryType <- "M" # Morning diary
diary_a$diaryType <- "A" # Afternoon diary
diary_e$diaryType <- "E" # Evening diary
# Merging diary data (LONG dataset)
diary <- plyr::join(plyr::join(diary_m,diary_a,type="full"),diary_e,type="full")
cat("sanity check:",nrow(diary) == nrow(diary_m) + nrow(diary_a) + nrow(diary_e))## sanity check: TRUE2.2.4. Data recoding
Then, we rename diary variables, we change the classes
of the relevant variables, and we recode the levels of categorical
(factor) variables consistently what we preregistered (see preregistration here).
# renaming columns
colnames(diary)[which(colnames(diary)=="lateWorkHours_1")] <- "lateWorkHours" # renaming column
colnames(diary)[which(colnames(diary)=="workHours_1")] <- "workHours" # renaming column
colnames(diary)[which(colnames(diary)=="waketime_16")] <- "wakeTime" # renaming column
colnames(diary) <- gsub("_","",colnames(diary))
colnames(diary)[grepl("SQ",colnames(diary))] <- paste("SQ",1:4,sep="")
colnames(diary)[which(colnames(diary)=="WL4")] <- "WL3"
colnames(diary)[grepl("WHLSM",colnames(diary))] <- paste("WHLSM",1:6,sep="")
colnames(diary)[which(colnames(diary)=="EE5")] <- "EE4"
colnames(diary)[grepl("R.det",colnames(diary))] <- paste("R.det",1:3,sep="")
colnames(diary)[grepl("R.rel",colnames(diary))] <- paste("R.rel",1:3,sep="")
colnames(diary)[grepl("R.mas",colnames(diary))] <- paste("R.mas",1:3,sep="")
# time (POSIXct variables)
diary$start <- as.POSIXct(diary$start)
diary$end <- as.POSIXct(diary$end)
# numeric
nums <- c(which(colnames(diary)=="SBP1"):which(colnames(diary)=="DBP2"),
which(colnames(diary)=="AWL1"):which(colnames(diary)=="AWL3"),
which(colnames(diary)=="OC1"):which(colnames(diary)=="SQ4"),
which(colnames(diary)=="WL1"):which(colnames(diary)=="R.mas3"),
which(colnames(diary)=="workHours"))
diary[,nums] <- lapply(diary[,nums],as.numeric)
# recording location
diary$where <- factor(gsub("1","workplace",
gsub("2","home",
gsub("4","other",diary$where))),levels=c("home","workplace","other"))
# dailyHassles
diary$dailyHassles <- as.factor(gsub("2","No",gsub("1","Yes",diary$dailyHassles)))
# diary type
diary$diaryType <- factor(diary$diaryType,levels=c("M","A","E"))
# dayOff_today & dayOff_yesterday as factor (recoding levels as FALSE = no, TRUE = yes)
diary$dayOff <- as.logical(as.numeric(diary$dayOff)-1)
diary$dayOfftoday <- as.logical(as.numeric(diary$dayOfftoday)-1)
diary$dayOffyesterday <- as.logical(as.numeric(diary$dayOffyesterday)-1)
# teleWork
diary$teleWork <- factor(gsub("1","teleWork",
gsub("2","office",
gsub("3","both",
gsub("4","dayOff",diary$teleWork)))),levels=c("office","teleWork","both","dayOff"))
# confounders
diary$confounders <- gsub("1","coffee",
gsub("2","smoke",
gsub("4","sport",
gsub("5","meal",
gsub("6","0",diary$confounders)))))2.2.5. Recoding confounders
The confounders variable is processed by using the
checklist_sep function in order to separate the different
confounding factors.
checklist_sep()
checklist_sep <- function(data=NULL,varName=NULL,sep=",",nOptions=4,
labels=c("coffee","smoke","sport","meal"),return.data=FALSE){
checklist <- data.frame(checklist_var=data[,varName])
checklist$checklist_var <- as.character(checklist$checklist_var)
for(i in 1:nOptions){ checklist$new.col <- NA
if(!is.null(labels)){ colnames(checklist)[i+1] <- labels[i] } else { colnames(checklist)[i+1] <- paste("item",i,sep="") }}
for(i in 1:nrow(checklist)){
if(!is.na(checklist[i,1])){ checklist[i,2:ncol(checklist)] <- 0
if(checklist[i,1]!="0"){ selected <- unlist(strsplit(checklist[i,1],sep))
for(k in 1:length(selected)){ checklist[i,selected[k]] <- 1 }}}}
checklist[,2:(nOptions+1)] <- lapply(checklist[,2:(nOptions+1)],as.factor)
# adding new variables to data (excluding the category without confounders)
data <- cbind(data[,1:which(colnames(data)==varName)],checklist[,2:(ncol(checklist)-1)],
data[,(which(colnames(data)==varName)+1):ncol(data)])
# recoding the original variable as logical (TRUE=any confounder category, FALSE=none)
data$originalVar <- data[,varName]
data[,varName] <- NA
data[!is.na(data$originalVar),varName] <- TRUE
data[!is.na(data$originalVar) & data$originalVar=="0",varName] <- FALSE
data$originalVar <- NULL
# printing summary
print(summary(na.omit(checklist[,1:5])))
# printing percentages
out <- paste("\n\nPercentage of",varName,"over total number of occasions with non-missing values:\n")
for(SC in labels){ colnames(checklist)[which(colnames(checklist)==SC)] <- "Var"
out <- paste(out,"- ",SC,": ",
round(100*nrow(checklist[!is.na(checklist$checklist_var) & checklist$Var == "1",])
/nrow(checklist[!is.na(checklist$checklist_var),]),2),
"%\n",sep="")
colnames(checklist)[which(colnames(checklist)=="Var")] <- SC }
cat(out)
if(return.data==TRUE){return(data)}}# number and percentages for each confounder
diary <- checklist_sep(data=diary,varName="confounders",return.data = TRUE)## checklist_var coffee smoke sport meal
## Length:2940 0:2700 0:2748 0:2843 0:2740
## Class :character 1: 240 1: 192 1: 97 1: 200
## Mode :character
##
##
## Percentage of confounders over total number of occasions with non-missing values:
## - coffee: 8.16%
## - smoke: 6.53%
## - sport: 3.3%
## - meal: 6.8%# overall No. of cases with one or more confounders (604 TRUE, 2336 FALSE, 153 NA)
summary(diary$confounders)## Mode FALSE TRUE NA's
## logical 2336 604 1532.2.6. wakeTime recoding
Then, we recode the wakeTime variable, which was
measured with an open-ended question.
# recoding wakeTime
wakeTimes <- diary$wakeTime
wakeTimes <- gsub("\\;",":",gsub("\\.","\\:",gsub("\\,","\\.",gsub("e",":",gsub(" ","",wakeTimes)))))
wakeTimes[!is.na(wakeTimes) & nchar(wakeTimes)==1] <- paste("0",wakeTimes[!is.na(wakeTimes) & nchar(wakeTimes)==1],":00",sep="")
wakeTimes[!is.na(wakeTimes) & nchar(wakeTimes)==2] <- paste(wakeTimes[!is.na(wakeTimes) & nchar(wakeTimes)==2],":00",sep="")
wakeTimes[!is.na(diary$wakeTime) & wakeTimes=="0615"] <- "05:15"
wakeTimes[!is.na(diary$wakeTime) & wakeTimes=="0800"] <- "08:00"
wakeTimes[!is.na(diary$wakeTime) & wakeTimes=="0830"] <- "08:30"
wakeTimes[!is.na(diary$wakeTime) & wakeTimes=="0700"] <- "07:00"
wakeTimes[!is.na(wakeTimes) & nchar(wakeTimes)==4] <- paste("0",wakeTimes[!is.na(wakeTimes) & nchar(wakeTimes)==4],sep="")
wakeTimes[!is.na(wakeTimes) & wakeTimes=="630"] <- "06:30"
# wakeTime as POSIXct & hhFromAwake
library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:data.table':
##
## hour, isoweek, mday, minute, month, quarter, second, wday, week,
## yday, year## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, uniondiary$wakeTime <- as.POSIXct(paste(substr(Sys.time(),1,10),wakeTimes),format="%Y-%m-%d %H:%M",tz="GMT")
diary$hhFromAwake <- as.numeric(difftime(as.POSIXct(paste(substr(Sys.time(),1,10),substr(diary$start,12,16)),
format="%Y-%m-%d %H:%M",tz="GMT"),diary$wakeTime,units="hours"))
# showing some example
head(diary[!is.na(diary$wakeTime),c("wakeTime","start","hhFromAwake")])2.3. Data anonymization
Third, participants’ ID (currently their e-mail
addresses) are anonymized (i.e., replaced with
alphanumeric code from S001 to Snnn).
Before anonymizing ID values, we filter cases only
included in one but not in the other dataset.
# preparing prelqs data
prelqs$ID <- tolower(prelqs$ID) # all e-mails in lower capital
prelqs.IDs <- levels(as.factor(prelqs$ID)) # saving original IDs
# excluding pre-pilot diary data with the main author
diary <- diary[!(grepl("ini.job",diary$ID)|grepl("varo@st",diary$ID)|grepl("tti@stu",diary$ID)|grepl("tti@un",diary$ID)),]
diary$ID <- tolower(diary$ID) # all e-mails in lower capital
# correcting wrongly reported e-mail addresses in diary data
diary$ID <- gsub("tierno@studio.unibo.it","tierno@libero.it",diary$ID)
diary$ID <- gsub("uzziell2","uzziello",diary$ID)
diary$ID <- gsub("uzziello@hotmail","uzziello@unibo",diary$ID)
diary$ID <- gsub("riarosa.m","rosa.m",gsub("o@auslromagna.it","o@gmail.com",diary$ID))
diary$ID <- gsub("o@auslcesena.it","o@gmail.com",diary$ID)
diary$ID <- gsub("gmsil","gmail",diary$ID)
diary$ID <- gsub("gmail.con","gmail.com",diary$ID)
diary$ID <- gsub("lli.robo@","lli.robi@",diary$ID)
diary$ID <- gsub("officia","officina",diary$ID)
diary$ID <- gsub("@liberi","@libero",gsub("@lidero","@libero",gsub("@llibro","@libero",diary$ID)))
diary$ID <- gsub("fcibien","f.cibien",gsub("klarco.com","klarco.it",diary$ID))
IDs <- levels(as.factor(diary$ID))
# sanity checks
cat(length(IDs),"participants in diary data,",length(prelqs.IDs),"in prelqs data\n",
length(IDs[!(IDs%in%prelqs.IDs)]),"cases in diary but not in prelqs\n",
length(prelqs.IDs[!(prelqs.IDs%in%IDs)]),"cases in prelqs but not in diary")## 151 participants in diary data, 149 in prelqs data
## 4 cases in diary but not in prelqs
## 2 cases in prelqs but not in diary# removing cases only included prelqs but not in diary
memory <- prelqs
prelqs <- prelqs[prelqs$ID %in% IDs,]
prelqs.IDs <- levels(as.factor(prelqs$ID))
cat("Removed",nrow(memory)-nrow(prelqs),"participants only included in prelqs data")## Removed 2 participants only included in prelqs data# removing cases only included in diary but not in prelqs
memory <- diary
diary <- diary[diary$ID %in% prelqs.IDs,]
IDs <- levels(as.factor(diary$ID))
cat("Removed",nlevels(as.factor(memory$ID))-length(IDs),"participants,",nrow(memory)-nrow(diary),
"observations only included in diary data")## Removed 4 participants, 4 observations only included in diary data# sanity checks
cat(length(IDs),"participants in diary data,",length(prelqs.IDs),"in prelqs data\n",
length(IDs[!(IDs%in%prelqs.IDs)]),"cases in diary but not in prelqs\n",
length(prelqs.IDs[!(prelqs.IDs%in%IDs)]),"cases in prelqs but not in diary")## 147 participants in diary data, 147 in prelqs data
## 0 cases in diary but not in prelqs
## 0 cases in prelqs but not in diaryWe also correct a number of issues with diary time
points due to participants answering to the wrong link.
# correcting surveys taken at wrong time points
diary[substr(diary$ID,15,27)=="rno@gmail.com" &
as.character(diary$start) %in% c("2021-10-26 21:26:43","2021-11-02 21:21:51"),
"diaryType"] <- "E" # participant who responded twice to Afternoon diary in Evening time (keeping only BP values)
diary[substr(diary$ID,15,27)=="rno@gmail.com" &
as.character(diary$start) %in% c("2021-10-26 21:26:43","2021-11-02 21:21:51"),
which(colnames(diary)=="WL1"):which(colnames(diary)=="WE3")] <- NA
diary[substr(diary$ID,8,13)=="isa.ma" & # participants that responded to a link open on the previous day
!is.na(diary$WHLSM3) & as.character(diary$start)=="2022-01-25 10:45:00",
"start"] <- as.character(as.POSIXct(diary[substr(diary$ID,8,13)=="isa.ma" & !is.na(diary$WHLSM3) &
as.character(diary$start)=="2022-01-25 10:45:00","end"]) -
mean(as.numeric(difftime(diary[substr(diary$ID,8,13)=="isa.ma" & diary$start!="2022-01-25 10:45:00","end"],
diary[substr(diary$ID,8,13)=="isa.ma" & diary$start!="2022-01-25 10:45:00","start"],units="secs"))))
diary <- diary[!(substr(diary$ID,8,13)=="mo@mda" & diary$start%in%c("2022-01-25 16:39:22","2022-01-26 20:00:21")),] # double resp
diary[grepl("aia79",diary$ID) & as.character(diary$start)=="2022-03-10 07:25:32","diaryType"] <-
"M" # participant that responded to the Evening diary in Morning time (keeping only BP values and changing as Morning)
diary[grepl("aia79",diary$ID) & as.character(diary$start)=="2022-03-10 07:25:32",
which(colnames(diary)=="EE1"):which(colnames(diary)=="workHours")] <- NA
diary <- # participant that responded twice to the Morning diary
diary[!(grepl("basso87",diary$ID) & as.character(diary$start)=="2022-03-07 19:53:47"),]
# correcting wrongly reported BP values
diary[substr(diary$ID,18,32)=="na.isasi.cnr.it" & as.character(diary$start)=="2021-11-25 19:17:27","SBP2"] <-
133 # reported as 881332.3.1. Preliminary questionnaire
Here, we anonymize prelqs ID values.
# replacing participants' e-mail with alphanumeric code (e.g., S001)
prelqs <- prelqs[order(prelqs$ID),] # sorting by ID
for(ID in prelqs.IDs){ id <- paste("S",which(prelqs.IDs==ID),sep="")
if(nchar(id)>2){ if(nchar(id)>3){ prelqs[prelqs$ID==ID,"ID"] <- id
} else { prelqs[prelqs$ID==ID,"ID"] <- gsub("S","S0",id) }
} else { prelqs[prelqs$ID==ID,"ID"] <- gsub("S","S00",id) }
prelqs[prelqs$ID==ID,"ID"] <- ifelse(nchar(id)==3,id,paste("S0",which(prelqs.IDs==ID),sep=""))}
prelqs$ID <- as.factor(prelqs$ID)
# sanity check
head(levels(prelqs$ID))## [1] "S001" "S002" "S003" "S004" "S005" "S006"cat(nlevels(prelqs$ID),"participants,",nrow(prelqs),"observations") # 147, 147## 147 participants, 147 observations2.3.2. Daily diaries
Here, we anonymize diary ID values.
# replacing participants' e-mail with alphanumeric code (e.g., S001)
diary <- diary[order(diary$ID,diary$start),] # sorting by ID and start time
for(ID in IDs){ id <- paste("S",which(IDs==ID),sep="") # replacing participants' e-mail with alphanumeric code (e.g., S001)
if(nchar(id)>2){ if(nchar(id)>3){ diary[diary$ID==ID,"ID"] <- id
} else { diary[diary$ID==ID,"ID"] <- gsub("S","S0",id) }
} else { diary[diary$ID==ID,"ID"] <- gsub("S","S00",id) }
diary[diary$ID==ID,"ID"] <- ifelse(nchar(id)==3,id,paste("S0",which(IDs==ID),sep=""))}
diary$ID <- as.factor(diary$ID)
# sanity check
head(levels(diary$ID))## [1] "S001" "S002" "S003" "S004" "S005" "S006"cat(nlevels(diary$ID),"participants,",nrow(diary),"observations") # 147,3077## 147 participants, 3075 observations3. Data processing
As a final pre-processing step, we process diary data by
including a day variable, by inspecting and recoding
BP (blood pressure) and BMI (body mass index)
data, by switching from the current long form to a wide
form with one row per day, by merging the diary
and the prelqs datasets, and by inspecting cases of
incoherent/inconsistent responses in the considered variables.
3.1. Assessment day
First, we create the variable day indicating the day for participation (i.e., 1 = Monday, 2 = Tuesday, 3 = Wednesday, 4 = Thursday, 5 = Friday, 7 = Monday week II, 8 = Tuesday week II, 9 = Wednesday week II, 10 = Thursday week II, 11 = Friday week II). Note that this variable is recoded to be referred to the previous day in cases of Evening diaries filled after 00:00, and in all Morning diaries. We also remove 5 unexpected responses on day 6 and 12 (Saturday afternoon or Sunday).
# creating day
diary$day <- as.POSIXlt(diary$start)$wday
# adding 6 days to the second week
for(i in 2:nrow(diary)){
if(diary[i,"ID"]==diary[i-1,"ID"] & diary[i,"day"] < diary[i-1,"day"]){ diary[i,"day"] <- diary[i,"day"] + 6 }}
# correcting evening days when start is after midnight
for(i in 1:nrow(diary)){
if(diary[i,"diaryType"]=="E" & diary[i,"start"] < as.POSIXct(paste(substr(diary[i,"start"],1,10),"07:00:00"))){
diary[i,"day"] <- diary[i,"day"] - 1 }}
# subtracting one day to all Morning diaries
diary[diary$diaryType=="M","day"] <- diary[diary$diaryType=="M","day"] - 1
# checking cases with unexpected values (6 or 12 = Morning on Mon, Evening/Afternoon on Sat)
table(diary$day)##
## 1 2 3 4 5 6 7 8 9 10 11 12
## 387 410 398 378 371 4 232 232 230 221 211 1# inspecting cases with unexpected values (6 or 12 = Morning on Mon, Evening/Afternoon on Sat)
cbind(wday=as.POSIXlt(diary[diary$day%in%c(6,12),"start"])$wday,
diary[diary$day%in%c(6,12),c("ID","day","start","SBP1","WL1","EE1","SQ1","dayOff","dayOffyesterday")])diary <- diary[!diary$day%in%c(6,12),] # removing 5 unexpected responses
# sanity check
diary[,c("ID","start","diaryType","day")]3.2. Blood pressure
Here, we visually inspect the distributions and temporal trends of
blood pressure BP values against
normative values and separately by the period
of the day. Then, we process the signal, and we compute the
aggregate BP values.
First, we prepare the data for the inspection and processing, by
creading a long form dataset with one row for each
BP recording.
# preparing dataset
diary_long <- data.frame(ID=rep(diary$ID,4),measure=c(rep("SBP1",nrow(diary)),rep("SBP2",nrow(diary)),
rep("DBP1",nrow(diary)),rep("DBP2",nrow(diary))),
BP=c(diary$SBP1,diary$SBP2,diary$DBP1,diary$DBP2),diaryType=rep(diary$diaryType,4))
diary_long$gender=plyr::join(diary_long,prelqs[,c("ID","gender")],type="left",by="ID")$gender
diary_long$cv_d=paste(plyr::join(diary_long,prelqs[,c("ID","cv_dysf")],type="left",by="ID")$cv_dysf,
plyr::join(diary_long,prelqs[,c("ID","bp_drugs")],type="left",by="ID")$bp_drugs,sep="_")3.2.1. BP distributions
Here, we visualize BP distributions by plotting them
against normative values.
vs. ESH/ESC Guidelines (2018)
First, we inspect the distribution of the observed BP
measurements, accounting for the cut-off values recommended by the ESH/ESC Guidelines (2018). We can see that
BP values are quite normally distributed (although with
some cases showing bimodal distribution) and centered below the cut-offs
for hypertension, with a small proportion of measurements (~
9-11%) above the cut-offs. Higher SBP and
DBP is showed in participants reporting suffering from
cardiovascular dysfunctions and/or taking BP medications. Slightly
higher systolic BP is reported by men compared to women,
and during afternoon compared to morning and evening. Overall, the data
look realistic, although diastolic BP looks bounded at 40
mmHg.
library(ggplot2); library(gridExtra)
# preparing dataset
diary_long <- data.frame(ID=rep(diary$ID,4),measure=c(rep("SBP1",nrow(diary)),rep("SBP2",nrow(diary)),
rep("DBP1",nrow(diary)),rep("DBP2",nrow(diary))),
BP=c(diary$SBP1,diary$SBP2,diary$DBP1,diary$DBP2),diaryType=rep(diary$diaryType,4))
diary_long$gender=plyr::join(diary_long,prelqs[,c("ID","gender")],type="left",by="ID")$gender
diary_long$cv_d=paste(plyr::join(diary_long,prelqs[,c("ID","cv_dysf")],type="left",by="ID")$cv_dysf,
plyr::join(diary_long,prelqs[,c("ID","bp_drugs")],type="left",by="ID")$bp_drugs,sep="_")
grid.arrange( # plotting sistolic and diastolic BP measurements
ggplot(na.omit(diary_long),aes(x=measure,y=BP,group=measure)) + geom_point(color="gray",position=position_jitter()) +
geom_violin(alpha=0.4) + geom_boxplot(alpha=0.4,outlier.alpha=0) + ylab("Blood pressure (mmHg)") +
geom_hline(yintercept = c(135,85),color="red",lty=2), # adding 2013 ESH/ESC Guidelines cut-offs for hypertension
ggplot(na.omit(diary_long),aes(x=measure,y=BP,fill=gender)) + geom_point(color="gray",position=position_jitter()) + # by gender
geom_violin(alpha=0.4) + geom_boxplot(alpha=0.4,outlier.alpha=0) + ylab("Blood pressure (mmHg)"),
ggplot(na.omit(diary_long),aes(x=measure,y=BP,fill=cv_d)) + geom_point(color="gray",position=position_jitter()) +
geom_violin(alpha=0.4) + geom_boxplot(alpha=0.4,outlier.alpha=0) + ylab("Blood pressure (mmHg)"), # by cv_dysf & bp_drugs
ggplot(na.omit(diary_long),aes(x=measure,y=BP,fill=diaryType)) + geom_point(color="gray",position=position_jitter()) +
geom_violin(alpha=0.4) + geom_boxplot(alpha=0.4,outlier.alpha=0) + ylab("Blood pressure (mmHg)"),nrow=2) # by time of day
# No. and % of SBP > 130 mmHG
ns <- nrow(diary_long[!is.na(diary_long$BP)&diary_long$measure%in%c("SBP1","SBP2")&diary_long$BP>135,])
nd <- nrow(diary_long[!is.na(diary_long$BP)&diary_long$measure%in%c("DBP1","DBP2")&diary_long$BP>85,])
cat(ns,"(",round(100*ns/nrow(diary_long[!is.na(diary_long$BP),]),1),"% ) SBP values above 130 mmHg\n",
nd,"(",round(100*nd/nrow(diary_long[!is.na(diary_long$BP),]),1),"% ) DBP values above 85 mmHg\n")## 1030 ( 8.7 % ) SBP values above 130 mmHg
## 1343 ( 11.4 % ) DBP values above 85 mmHg# minumum diastolic BP
min(diary_long[diary_long$measure%in%c("DBP1","DBP2"),"BP"],na.rm=TRUE)## [1] 40vs. Weisser et al (2000)
Here, we compare the observed BP values with normative values from Weisser et al (2000), based on self-measured and office BP from 503 healthy Swiss adults. Red lines indicate the 35°, 58° and 76° percentile for office (solid) and self-measured (dotted) systolic BP, and the 50°, 67° and 78° percentile for diastolic BP, respectively. White dotted lines indicate the corresponding percentiles in the current sample. We can see that the observed values are quite similar to the normative ones, with greater similarities with home (~3-5 mmHg) compared to office normative values (~9 mmHg). It is confirmed that DBP is bounded at 40 mmHg, with a peak of cases corresponding to the lower limit.
par(mfrow=c(2,2))
for(BP in c("SBP1","DBP1","SBP2","DBP2")){ hist(diary[,BP],main=BP,xlab="BP (mmHg)",breaks=50,col="black")
abline(v=quantile(diary[,BP],na.rm=TRUE,probs=c(.35,.58,.76)),col="gray",lwd=2,lty=2) # observed quantiles
if(substr(BP,1,1)=="S"){ abline(v=c(114.3,124.1,132.6),col="red",lwd=2,lty=2) # normative quantiles (systolic, home)
abline(v=c(120,130,140),col="red",lwd=3) # normative quantiles (systolic, office)
} else { abline(v=c(75.1,79.9,85.8),col="red",lwd=2,lty=2) # normative quantiles (diastolic, home)
abline(v=c(80,85,90),col="red",lwd=3) }} # normative quantiles (diastolic, office)ì
vs. Asayama et al (2019)
Then, we compare BP values obtained from Morning and Evening measures with those reported by Asayama et al (2019) for morning and evening BP self-measured by 308 hypertensive patients. Red lines indicate normative mean ± 2 SD, whereas gray dotted lines indicate the observed mean +/- 2 SD from the current sample. Again, data look ‘realistic’.
par(mfrow=c(2,2))
for(BP in c("SBP1","SBP2","DBP1","DBP2")){ hist(diary[,BP],main=BP,xlab="BP (mmHg)",breaks=50,col="black")
abline(v=c(mean(diary[,BP],na.rm=TRUE),mean(diary[,BP],na.rm=TRUE)-2*sd(diary[,BP],na.rm=TRUE),
mean(diary[,BP],na.rm=TRUE)+2*sd(diary[,BP],na.rm=TRUE)),col="gray",lwd=2,lty=2)
if(substr(BP,1,1)=="S"){ abline(v=c(128.0,128.0+9.4*2,128.0-9.4*2),col="red",lwd=2)
} else { abline(v=c(70.5,70.5+9.7*2,70.5-9.7*2),col="red",lwd=2) }} # normative mean +/- sd (diastolic)
3.2.2. BP by day time
Here, we visualize the distribution of Systolic BP values in Morning (M), Afternoon (A) and Evening (E) recordings, while also showing individual patterns of change. It is hard to determinate the prevalence of an increasing or decreasing pattern for both variables, although most cases seem to increase from Morning to Afternoon and decrease from Afternoon to Evening.
# plotting SBP
diary$IDday <- as.factor(paste(diary$ID,diary$day,sep="_")) # adding ID x day factor
grid.arrange(ggplot(data=diary,aes(x=diaryType,y=SBP1)) + geom_line(aes(group=IDday,col=IDday)) + geom_violin(alpha=.4) +
geom_boxplot(alpha=0.5,width=0.3,outlier.alpha=0) + theme(legend.position="none"),
ggplot(data=diary,aes(x=diaryType,y=SBP2)) + geom_line(aes(group=IDday,col=IDday)) + geom_violin(alpha=.4) +
geom_boxplot(fill=rgb(1,1,1,alpha=.4),width=0.3,outlier.alpha=0) + theme(legend.position="none"),nrow=1)
# plotting DBP
grid.arrange(ggplot(data=diary,aes(x=diaryType,y=DBP1)) + geom_line(aes(group=IDday,col=IDday)) + geom_violin(alpha=.4) +
geom_boxplot(fill=rgb(1,1,1,alpha=.4),width=0.3) + ggtitle("Diastolic BP 1") + theme(legend.position="none"),
ggplot(data=diary,aes(x=diaryType,y=DBP2)) + geom_line(aes(group=IDday,col=IDday)) + geom_violin(alpha=.4) +
geom_boxplot(fill=rgb(1,1,1,alpha=.4),width=0.3) + ggtitle("Diastolic BP 2") + theme(legend.position="none"),nrow=1)
3.2.3. BP trajectories
Then, we use the plotBP function to visualize daily
fluctuations in BP values. For each recording, the first and second
entered value are showed in light and dark blue, respectively. The red
triangles show cases in which the second measurement was 10 mmHg higher
or lower than the second one, whereas the blue dotted line indicates the
ESH/ESC Guidelines (2018) cut-off for hypertension.
For both systolic and diastolic BP time series, we flag
problematic cases based on the visual inspection of steep BP
increases/decreases compared to the others values from that
participant.
plotBP()
plotBP <- function(data=NULL,BP="S",ncol=3,flagged=NA,what=NA,meanVal=NA){ require(ggplot2)
# creating TIME by joining day and diaryType
data$TIMElab <- paste(data$day,data$diaryType,sep="")
data$TIME <- as.numeric(gsub("M",".0",gsub("A",".33",gsub("E",".66",data$TIMElab))))
# setting graphic parameters
cols <- colnames(data)[substr(colnames(data),1,3)==paste(BP,"BP",sep="")] # columns to be plotted
maxScore <- max(data[,cols],na.rm=TRUE)
data$disc <- abs(data[,cols[1]]-data[,cols[2]])
data$disc10 <- FALSE
data[!is.na(data$disc) & data$disc>=10,"disc10"] <- TRUE
# plotting
p <- ggplot(data,aes_string(x="TIME",y=cols[1])) +
geom_line(colour="lightblue") + geom_point(colour="lightblue") +
scale_x_continuous(breaks=seq(1.33,12.66,length.out=11),labels=paste(1:11,"A",sep=""),position="top") +
geom_line(aes_string(y=cols[2]),colour="blue") +
geom_point(aes_string(y=cols[2],shape="disc10"),size=2,color="blue") +
geom_point(data=data[!is.na(data$disc) & data$disc>=10,],aes_string(y=cols[2],shape="disc10"),size=2,color="red") +
facet_wrap("ID",strip.position = "right",ncol=ncol) +
ggtitle(paste("Daily fluctuations in",ifelse(BP=="S","Systolic","Diastolic"),
"blood pressure (light blue = 1st recording, dark blue = 2nd recording, red triangles = discrep. > 10 mmHg)")) +
xlab("Day of week") + ylab(paste(BP,"BP (mmHg)",sep=""))+
geom_hline(yintercept=ifelse(BP=="S",130,85),color="blue",lty=2)+
theme(axis.text.x = element_text(size=9,angle = 45),legend.position="none")
# marking flagged cases
if(!is.na(flagged)){ data$flag <- data[,flagged]
p <- p + geom_point(data=data[!is.na(data$flag) & data$flag==TRUE,],aes_string(y=cols[2]),size=4,shape=1) +
geom_text(data=data[!is.na(data$flag) & data$flag==TRUE,],aes_string(y=paste(cols[2],"+10"),label=what)) }
# showing average value
if(!is.na(meanVal)){ data$meanVal <- data[,meanVal]
p <- p + geom_line(data=data[!is.na(data$meanVal),],aes(y=meanVal),lwd=1.1,color="green") +
geom_point(data=data[!is.na(data$meanVal),],aes(y=meanVal),color="green") }
p}SBP
plotBP(diary,BP="S",ncol=4)
DBP
plotBP(diary,BP="D",ncol=4)
SBP (flagged)
Here, we manually flag the cases showing extreme values in the second measurement or both, since we will remove the first measurement when it is more than 10 mmHg lower or higher than the first one (see below). Only these cases are plotted below.
# flagged cases (only second = "2" or "both" measurement)
flagS <- data.frame(ID = c("S006","S007","S011","S011","S030","S030","S033","S058","S061","S065","S077","S077"),
day = c(3, 2, 1, 8, 1, 2, 6, 8, 9, 10, 1, 1),
diaryType= c("A", "E", "A", "A", "A", "A", "M", "A", "E", "M", "A", "E"),
whatS = c("both","both","both","2", "2", "2", "2", "2", "2", "2", "2", "2"))
# further flagged cases
flagS <- rbind(flagS,
data.frame(ID = c("s080","s080","s080","S082","S100","S110","S110","S144"),
day = c( 1, 1, 2, 1, 1, 2, 3, 8),
diaryType= c( "E", "M", "E", "A", "A", "M", "M", "A"),
whatS = c( "both","both","both","both","both", "2", "2", "both")))
# adding information on flagged cases
flagS$SBPflag <- TRUE
diary <- plyr::join(diary,flagS,by=c("ID","day","diaryType"),type="left")
diary[is.na(diary$SBPflag) & !is.na(diary$SBP1),"SBPflag"] <- FALSE
# plotting
plotBP(diary[diary$ID%in%levels(as.factor(as.character(flagS$ID))),],BP="S",ncol=4,flagged="SBPflag",what="whatS")
DBP (flagged)
Here, we manually flag the cases showing extreme values in the second measurement or both, since we will remove the first measurement when it is more than 10 mmHg lower or higher than the first one (see below). Only these cases are plotted below.
# flagged cases (only systolic = "S" or "both", but not diastolic - see below)
flagD <- data.frame(ID = c(rep("S006",4), "S007","S034","S057","S058","S058","S070","S070",rep("S080",3),"S082"),
day = c( 1, 2, 2, 3, 5, 2, 9, 8, 8, 6, 8, 1,2,2, 1),
diaryType=c( "A","M", "E","M", "E", "M", "E", "A", "E", "M", "M", "E","M","E","A"),
whatD = c("2","inv","2","2", "both", "2", "2", "2", "2", "2", "2", rep("both",3),"both"))
# further flagged cases
flagD <- rbind(flagD,
data.frame(ID=c("S092","S093","S093","S093","S117","S117","S130","S144","S144","S145"),
day = c( 9, 2, 3, 7, 6, 9, 2, 8, 11, 1),
diaryType = c("E", "A", "A", "E", "M", "A", "E", "A", "E", "A"),
whatD = c( "2", "inv", "inv", "inv", "2", "2", "2", "both","both", "2")))
# adding information on flagged cases
flagD$DBPflag <- TRUE
diary <- plyr::join(diary,flagD,by=c("ID","day","diaryType"),type="left")
diary[is.na(diary$DBPflag) & !is.na(diary$SBP1),"DBPflag"] <- FALSE
# plotting
plotBP(diary[diary$ID%in%levels(as.factor(as.character(flagD$ID))),],BP="D",ncol=4,flagged="DBPflag",what="whatD")
3.2.4. BP Aggregated values
Here, we aggregate the first and second measurement of systolic and
diastolic BP. As pre-registered here, SBP and DBP
aggregated values are computed as the average between the first
and the second (immediately consecutive) BP
recording. In all cases showing discrepancies (i.e.,
absolute differences) between the first and the second recording
equal to or higher than 10 mmHg, only the second recorded
value is considered (i.e., the first recording is
excluded).
However, we also account for the flagged cases marked as “2” (when the second measurement should be removed), “both” (when both measurements have extreme values in the same direction), and “inv” (when the both have extreme values in opposite directions):
in cases flagged as
"2", we only keep the first measurementin cases flagged as
"inv", we compute the average (as in any non-flagged case)in cases flagged as
"both", we interpolate the measurement as the maximum value (or the minimum, based on the direction of extreme values) recorded for that participant.
This is done independently for systolic and diastolic
BP
3.2.4.1. Aggregating values
First, we aggregate SBP values.
# SBP - printing info
cat("- Averaging",nrow(diary[!is.na(diary$SBPflag) & (diary$SBPflag==FALSE | diary$SBPflag=="inv") &
abs(diary$SBP1-diary$SBP2)<10,]),"cases\n- Excluding",
nrow(diary[!is.na(diary$SBPflag) & abs(diary$SBP1-diary$SBP2)>=10 & diary$SBPflag==FALSE,]),
"first measurments\n- Excluding",nrow(diary[!is.na(diary$whatS) & diary$whatS=="2",]),
"second mesurements\n- Interpolating",nrow(diary[!is.na(diary$whatS) & diary$whatS=="both",]),"measurements")## - Averaging 2366 cases
## - Excluding 562 first measurments
## - Excluding 10 second mesurements
## - Interpolating 6 measurements# (1) averaged cases (no flagged, or flagged = "inv", and discrepancies < 10mmHg)
diary[(!is.na(diary$SBPflag) & diary$SBPflag==FALSE & abs(diary$SBP1-diary$SBP2) < 10) |
(!is.na(diary$whatS) & diary$whatS=="inv"),"SBP"] <-
apply(diary[(!is.na(diary$SBPflag) & diary$SBPflag==FALSE & abs(diary$SBP1-diary$SBP2) < 10) |
(!is.na(diary$whatS) & diary$whatS=="inv"),c("SBP1","SBP2")],1,mean)
# (2) cases only based on the second measuremnt (discrepancies >= 10mmHg AND no flags)
diary[!is.na(diary$SBPflag) & diary$SBPflag==FALSE & abs(diary$SBP1-diary$SBP2) >= 10,"SBP"] <-
diary[!is.na(diary$SBPflag) & diary$SBPflag==FALSE & abs(diary$SBP1-diary$SBP2) >= 10,"SBP2"]
# (3) cases only based on the first measuremnt (flagged=TRUE and what = "2")
diary[!is.na(diary$SBPflag) & diary$SBPflag==TRUE & diary$whatS=="2",
"SBP"] <- diary[!is.na(diary$SBPflag) & diary$SBPflag==TRUE & diary$whatS=="2","SBP1"]
# (4) cases interpolated as the maximum value
for(i in which(!is.na(diary$whatS) & diary$whatS=="both")){
if(diary[i,"SBP2"]>mean(diary[diary$ID==diary[i,"ID"],"SBP"],na.rm=TRUE)){
diary[i,"SBP"] <- max(diary[diary$ID==diary[i,"ID"],"SBP"],na.rm=TRUE)
} else { diary[i,"SBP"] <- min(diary[diary$ID==diary[i,"ID"],"SBP"],na.rm=TRUE) }}
# sanity check
nrow(diary[(!is.na(diary$SBPflag) & is.na(diary$SBP))|(is.na(diary$SBPflag) & !is.na(diary$SBP)),]) == 0 ## [1] TRUEThen, we aggregate DBP values.
# DBP - printing info
cat("- Averaging",nrow(diary[((!is.na(diary$DBPflag) & diary$DBPflag==FALSE) |
(!is.na(diary$whatD) & diary$whatD=="inv")) &
abs(diary$DBP1-diary$DBP2)<10,]),"cases\n- Excluding",
nrow(diary[!is.na(diary$DBPflag) & abs(diary$DBP1-diary$DBP2)>=10 & diary$DBPflag==FALSE,]),
"first measurments\n- Excluding",nrow(diary[!is.na(diary$whatD) & diary$whatD=="2",]),
"second mesurements\n- Interpolating",nrow(diary[!is.na(diary$whatD) & diary$whatD=="both",]),"measurements")## - Averaging 2292 cases
## - Excluding 629 first measurments
## - Excluding 12 second mesurements
## - Interpolating 7 measurements# (1) averaged cases (no flagged and discrepancies < 10mmHg, or flagged = "inv")
diary[((!is.na(diary$DBPflag) & diary$DBPflag==FALSE & abs(diary$DBP1-diary$DBP2) < 10) |
(!is.na(diary$whatD) & diary$whatD=="inv")),"DBP"] <-
apply(diary[((!is.na(diary$DBPflag) & diary$DBPflag==FALSE & abs(diary$DBP1-diary$DBP2) < 10) |
(!is.na(diary$whatD) & diary$whatD=="inv")),c("DBP1","DBP2")],1,mean)
# (2) cases only based on the second measurement (discrepancies >= 10mmHg AND no flags)
diary[!is.na(diary$DBPflag) & diary$DBPflag==FALSE & abs(diary$DBP1-diary$DBP2) >= 10,"DBP"] <-
diary[!is.na(diary$DBPflag) & diary$DBPflag==FALSE & abs(diary$DBP1-diary$DBP2) >= 10,"DBP2"]
# (3) cases only based on the first measurement (flagged=TRUE and what = "2")
diary[!is.na(diary$DBPflag) & !is.na(diary$whatD) & diary$DBPflag==TRUE & diary$whatD=="2",
"DBP"] <- diary[!is.na(diary$DBPflag) & diary$DBPflag==TRUE & diary$whatD=="2","DBP1"]
# (4) cases interpolated as the maximum value
for(i in which(!is.na(diary$whatD) & diary$whatD=="both")){
if(diary[i,"DBP2"]>mean(diary[diary$ID==diary[i,"ID"],"DBP"],na.rm=TRUE)){
diary[i,"DBP"] <- max(diary[diary$ID==diary[i,"ID"],"DBP"],na.rm=TRUE)
} else { diary[i,"DBP"] <- min(diary[diary$ID==diary[i,"ID"],"DBP"],na.rm=TRUE) }}
# sanity check
nrow(diary[(!is.na(diary$DBPflag) & is.na(diary$DBP))|(is.na(diary$DBPflag) & !is.na(diary$DBP)),]) == 0 ## [1] TRUE3.2.4.2. Visual inspection
Then, we replicate the plots generated above by showing the aggregated BP values in green.
SBP
plotBP(diary,BP="S",ncol=4,flagged="SBPflag",what="whatS",meanVal="SBP")
DBP
plotBP(diary,BP="D",ncol=4,flagged="DBPflag",what="whatD",meanVal="DBP")## Warning: Removed 126 rows containing missing values (`geom_point()`).
## Removed 126 rows containing missing values (`geom_point()`).
3.2.4.3. Data integration
Finally, we replace the raw BP data with the processed (i.e.,
aggregate) values in the diary dataset and we remove the
variables identifying flagged cases.
# replacing raw BP with aggregate BP
diary <- diary[,c(1:which(colnames(diary)=="dayOfftoday"),which(colnames(diary)%in%c("SBP","DBP")),
which(colnames(diary)=="dayOffyesterday"):(ncol(diary)-2))]
# creating new variable summarizing flagged cases
diary$flagBP <- FALSE
diary[(!is.na(diary$SBPflag) & diary$SBPflag==TRUE)|(!is.na(diary$DBPflag) & diary$DBPflag==TRUE),"flagBP"] <- TRUE
diary$SBPflag <- diary$whatS <- diary$DBPflag <- diary$whatD <- NULL # removing variables identifying flagged cases3.3. BMI
Here, we inspect the distribution of BMI values, and we
compare it with the cut-offs proposed by the World Health
Organization (2010). We observe no extreme BMI
values despite a few participants in the underweight (N = 3),
and obesity - class II category (N = 8), and one participant in the
obese - class III category. We have no reasons to
exclude participants based on BMI values.
hist(prelqs$BMI,xlab="BMI (kg/m^2)",breaks=30,xlim=c(15,40),ylim=c(0,14))
abline(v=c(18.5,25,30,35,40),col="red",lty=2)
text(x=c(16,21.5,27.5,32.5,37.5),y = 13,
labels=c("Underweight","Normal weight","Pre-obesity","Obesity I","Obesity II"))
3.4. Daily wide dataset
Here, we use the day variable created above to transform
the diary dataset from the current long form (i.e., one row
per measurement occasion within day and participant) to a daily wide
form (i.e., one row per day within participant, with measurements
collected on the same day being presented in the same line of the
dataset).
First, we split the diary dataset back to the three
datasets based on the measurement occasion (i.e., morning, afternoon,
and evening).
# sorting columns
diary <- diary[,c("ID","day","start","end","diaryType","dayOff","dayOfftoday","dayOffyesterday",
"SBP","DBP","flagBP","where","confounders","coffee","smoke","sport","meal","dailyHassles",
colnames(diary)[which(colnames(diary)=="OC1"):which(colnames(diary)=="wakeTime")],"hhFromAwake",
paste("AWL",1:3,sep=""),
colnames(diary)[which(colnames(diary)=="WL1"):which(colnames(diary)=="workHours")],"IDday")]
# splitting dataset
diary_m <- diary[diary$diaryType=="M",c(1:which(colnames(diary)=="end"),
which(colnames(diary)=="dayOfftoday"):which(colnames(diary)=="AWL3"),ncol(diary))]
diary_a <- diary[diary$diaryType=="A",c(1:which(colnames(diary)=="end"),which(colnames(diary)=="dayOff"),
which(colnames(diary)=="SBP"):which(colnames(diary)=="meal"),
which(colnames(diary)=="WL1"):which(colnames(diary)=="WE3"),ncol(diary))]
diary_e <- diary[diary$diaryType=="E",c(1:which(colnames(diary)=="end"),which(colnames(diary)=="dayOff"),
which(colnames(diary)=="SBP"):which(colnames(diary)=="flagBP"),
which(colnames(diary)=="confounders"):which(colnames(diary)=="dailyHassles"),
which(colnames(diary)=="EE1"):which(colnames(diary)=="workHours"),ncol(diary))]
# renaming columns sharing the same name in the three datasets
cm <- colnames(diary_m)
ca <- colnames(diary_a)
ce <- colnames(diary_e)
colnames(diary_m)[3:ncol(diary_m)][which(colnames(diary_m)[3:ncol(diary_m)]%in%ca | colnames(diary_m)[3:ncol(diary_m)]%in%ce)] <-
paste(colnames(diary_m)[3:ncol(diary_m)][which(colnames(diary_m)[3:ncol(diary_m)]%in%ca |
colnames(diary_m)[3:ncol(diary_m)]%in%ce)],"mor",sep="_")
colnames(diary_a)[3:ncol(diary_a)][which(colnames(diary_a)[3:ncol(diary_a)]%in%cm | colnames(diary_a)[3:ncol(diary_a)]%in%ce)] <-
paste(colnames(diary_a)[3:ncol(diary_a)][which(colnames(diary_a)[3:ncol(diary_a)]%in%cm |
colnames(diary_a)[3:ncol(diary_a)]%in%ce)],"aft",sep="_")
colnames(diary_e)[3:ncol(diary_e)][which(colnames(diary_e)[3:ncol(diary_e)]%in%cm | colnames(diary_e)[3:ncol(diary_e)]%in%ca)] <-
paste(colnames(diary_e)[3:ncol(diary_e)][which(colnames(diary_e)[3:ncol(diary_e)]%in%cm |
colnames(diary_e)[3:ncol(diary_e)]%in%ca)],"eve",sep="_")
# adding IDday column to join datasets based on ID and day
colnames(diary_m)[ncol(diary_m)] <- colnames(diary_a)[ncol(diary_a)] <- colnames(diary_e)[ncol(diary_e)] <- "IDday"Second, we look for cases of double responses within
each dataset by using the ID x day identifier
created above. A total of six double responses are detected.
# diary_m: 1 double response -> taking 1st one
diary_m[diary_m$IDday%in%diary_m$IDday[duplicated(diary_m$IDday)],] diary_m <- diary_m[!(diary_m$ID=="S025" & as.character(diary_m$end_mor)=="2022-05-03 13:53:34"),]
# diary_a: 2 double responses -> taking 1st or 2d based on submission time
diary_a[diary_a$IDday%in%diary_a$IDday[duplicated(diary_a$IDday)],] diary_a <- diary_a[!((diary_a$ID=="S040" & as.character(diary_a$end_aft)=="2022-03-08 20:01:01") |
(diary_a$ID=="S060" & as.character(diary_a$end_aft)=="2022-01-25 18:58:10") |
(diary_a$ID=="S113" & as.character(diary_a$end_aft)=="2022-02-28 20:33:41") |
(diary_a$ID=="S113" & as.character(diary_a$end_aft)=="2022-03-01 21:41:58")),]
# diary_e: 3 double responses -> taking 2nd or 1st based on submission time
diary_e[diary_e$IDday%in%diary_e$IDday[duplicated(diary_e$IDday)],] diary_e <- diary_e[!((diary_e$ID=="S060" & as.character(diary_e$end_eve)=="2022-01-26 19:57:02") |
(diary_e$ID=="S108" & diary_e$dayOff_eve==TRUE)),]
# sanity check
nrow(diary_m[duplicated(diary_m$IDday),]) == 0 & nrow(diary_a[duplicated(diary_a$IDday),]) == 0 &
nrow(diary_e[duplicated(diary_e$IDday),]) == 0## [1] TRUENow we can merge the tree dataset into a daily wide form dataset.
# joining datasets
diary_wide <- plyr::join(diary_a,plyr::join(diary_e,diary_m,by="IDday",type="full"),by="IDday",type="full")
diary_wide <- diary_wide[order(diary_wide$ID,diary_wide$day),] # sorting by ID and day
# sanity check
nrow(diary_wide[!is.na(diary_wide$start_aft),]) == nrow(diary_a) &
nrow(diary_wide[!is.na(diary_wide$start_eve),]) == nrow(diary_e) &
nrow(diary_wide[!is.na(diary_wide$start_mor),]) == nrow(diary_m)## [1] TRUE# checking for duplicates
nrow(diary_wide[duplicated(diary_wide$IDday),])## [1] 0diary_wide$IDday <- NULL # removing IDday (no longer necessary)
# adding variables to filter missing data
diary_wide[,c("aft","eve","mor")] <- 0
diary_wide[!is.na(diary_wide$start_aft) & !is.na(diary_wide$dayOff_aft) & diary_wide$dayOff_aft==FALSE,"aft"] <- 1
diary_wide[!is.na(diary_wide$start_eve) & !is.na(diary_wide$dayOff_eve) & diary_wide$dayOff_eve==FALSE,"eve"] <- 1
diary_wide[!is.na(diary_wide$start_mor) & !is.na(diary_wide$dayOffyesterday) & diary_wide$dayOffyesterday==FALSE,"mor"] <- 1
diary_wide[,c("aft","eve","mor")] <- lapply(diary_wide[,c("aft","eve","mor")],as.factor)
diary_wide <- diary_wide[,c(1:2,(ncol(diary_wide)-2):ncol(diary_wide),3:(ncol(diary_wide)-3))]
# sorting by ID and day
diary_wide <- diary_wide[order(diary_wide$ID,diary_wide$day),]
rownames(diary_wide) <- 1:nrow(diary_wide)
# showing dataset
diary_wide[,c(colnames(diary_wide)[1:6],"start_eve","start_mor")]3.5. Joining prelqs
Here, we join the demographic and occupational information from the
prelqs dataset with the time-varying data included in the
diary_wide dataset. The resulting diary_wide
dataset has multiple rows reporting the same demographic and
occupational values (level-2) repeated over multiple rows associated
with the same participant. Moreover, we also join the responses to the
raw retrospective workaholism items.
# renaming retrospective workaholism items
colnames(prelqs)[grep("WHLSM",colnames(prelqs))] <- paste0("duwas",1:10)
# joining prelqs variables to diary_wide
diary_wide <- plyr::join(diary_wide,
prelqs[,c("ID",
colnames(prelqs)[which(colnames(prelqs)=="gender"):which(colnames(prelqs)=="children")],
"home_child","partner","home_partner",
colnames(prelqs)[which(colnames(prelqs)=="smoker"):which(colnames(prelqs)=="workTime")],
paste0("duwas",1:10))],
by="ID",type="left") # merging datasets by ID
# showing examples
head(diary_wide[,c(colnames(diary_wide)[1:6],"SBP_aft","age","sector")])3.6. Response processing
Here, we look for cases of incoherent/inconsistent responses in several considered variables.
3.6.1. Days off
First, we check the cases of incoherent responses to the
dayOff variable across the three daily diaries (e.g.,
participants reporting “today, I did not work” to the afternoon
but not to the evening questionnaire). We can see a few incoherent cases
(2%), mainly between afternoon and morning, in which
participants reported a day-off at the end of the workday but an
incoherent response on the following day. Since we have no criteria for
establishing which was the ‘true’ answer, we assume that (un)voluntary
mistakes were made for dayOff=TRUE responses,
treating them as missing responses. Note that none of
the cases with dayOff_eve=FALSE have
workHours=0.
# 1) inconsistencies within Evening: dayOff_eve = FALSE but teleWork = "dayOff" (N = 4)
diary_wide[!is.na(diary_wide$teleWork) & diary_wide$teleWork=="dayOff",c("ID","day","teleWork","workHours")]diary_wide[!is.na(diary_wide$teleWork) & diary_wide$teleWork=="dayOff","teleWork"] <- "office" # recoding as teleWork="office"
# 2) inconsistencies between Afternoon & Evening: dayOff_aft != dayOff_eve (N = 10)
diary_wide[!is.na(diary_wide$dayOff_aft) & !is.na(diary_wide$dayOff_eve) & diary_wide$dayOff_aft != diary_wide$dayOff_eve,
c("ID","day","teleWork","workHours","aft","WL1","eve","EE1","dayOff_eve","dayOff_aft")]diary_wide[!is.na(diary_wide$dayOff_aft) & !is.na(diary_wide$dayOff_eve) &
diary_wide$dayOff_aft==TRUE & diary_wide$dayOff_eve == FALSE,"dayOff_aft"] <- NA # replacing incorrect dayOff as NA
diary_wide[!is.na(diary_wide$dayOff_aft) & !is.na(diary_wide$dayOff_eve) &
diary_wide$dayOff_aft==FALSE & diary_wide$dayOff_eve == TRUE,"dayOff_eve"] <- NA
# 3) inconsistencies between Afternoon & Evening: dayOff_aft = FALSE but teleWork = "dayOff" (N = 0)
diary_wide[!is.na(diary_wide$dayOff_aft) & !is.na(diary_wide$teleWork) &
diary_wide$dayOff_aft==FALSE & diary_wide$teleWork=="dayOff",
c("ID","day","teleWork","workHours","aft","WL1","eve","EE1","dayOff_eve","dayOff_aft")]# 4) inconsistencies between Afternoon & Morning: dayOff_aft != dayOffyesterday (N = 4)
diary_wide[!is.na(diary_wide$dayOff_aft) & !is.na(diary_wide$dayOffyesterday) & diary_wide$dayOff_aft!=diary_wide$dayOffyesterday,
c("ID","day","workHours","aft","WL1","mor","SQ1","dayOffyesterday","dayOff_aft")]diary_wide[!is.na(diary_wide$dayOff_aft) & !is.na(diary_wide$dayOffyesterday) &
diary_wide$dayOff_aft==TRUE & diary_wide$dayOffyesterday == FALSE,"dayOff_aft"] <- NA # replacing with NA
diary_wide[!is.na(diary_wide$dayOff_aft) & !is.na(diary_wide$dayOffyesterday) &
diary_wide$dayOff_aft==FALSE & diary_wide$dayOffyesterday == TRUE,"dayOffyesterday"] <- NA
# 5) inconsistencies between Evening & Morning: dayOff_eve != dayOffyesterday (N = 2)
diary_wide[!is.na(diary_wide$dayOff_eve) & !is.na(diary_wide$dayOffyesterday) & diary_wide$dayOff_eve!=diary_wide$dayOffyesterday,
c("ID","day","workHours","eve","EE1","mor","SQ1","dayOffyesterday","dayOff_eve")]diary_wide[!is.na(diary_wide$dayOff_eve) & !is.na(diary_wide$dayOffyesterday) & diary_wide$dayOff_eve!=diary_wide$dayOffyesterday,
"dayOff_eve"] <- NA # replacing unexpected value with NA
# 6) inconsistencies between Evening & Morning: teleWork = "dayOff" but dayOffyesterday=FALSE (N = 0)
diary_wide[!is.na(diary_wide$dayOffyesterday) & !is.na(diary_wide$teleWork) &
diary_wide$dayOffyesterday==FALSE & diary_wide$teleWork=="dayOff",
c("ID","day","teleWork","workHours","eve","EE1","mor","SQ1","dayOff_eve","dayOffyesterday")]Similarly, we inspect cases of participants reporting working days
but answering “Today I did not work” to the teleWork item
in the evening questionnaire, and vice versa. We can note that there are
no cases of incoherent responses related to these
variables.
# evening dayOff incoherent to evening telework (N = 0)
diary_wide[!is.na(diary_wide$dayOff_eve) & !is.na(diary_wide$telework) &
((diary_wide$dayOff_eve==FALSE & diary_wide$teleWork=="dayOff") |
(diary_wide$dayOff_eve==TRUE & diary_wide$teleWork!="dayOff")),
c("ID","dayOff_eve","teleWork","workHours")]# afternoon dayOff incoherent to evening telework (N = 0)
diary_wide[!is.na(diary_wide$dayOff_aft) & !is.na(diary_wide$telework) &
((diary_wide$dayOff_aft==FALSE & diary_wide$teleWork=="dayOff") |
(diary_wide$dayOff_aft==TRUE & diary_wide$teleWork!="dayOff")),
c("ID","dayOff_aft","teleWork","workHours")]# morning dayOffyesterday incoherent to evening telework (N = 0)
diary_wide[!is.na(diary_wide$dayOffyesterday) & !is.na(diary_wide$telework) &
((diary_wide$dayOffyesterday==FALSE & diary_wide$teleWork=="dayOff") |
(diary_wide$dayOffyesterday==TRUE & diary_wide$teleWork!="dayOff")),
c("ID","dayOffyesterday","teleWork","workHours")]Finally, we inspect all cases with workHours = 0. We can
see only a few of such cases (2%), all of which show
dayOff_eve=FALSE (also because it was a necessary condition
for responding to the workHours question) as well as
dayOff_aft=FALSE and dayOffyesterday=FALSE.
Due to this incoherence, we ‘trust’ the responses to dayOff
items and we assume that these workHours responses were
careless responses. Thus, we
interpolate them by assigning the mean No.of
workHours reported by the same participant in the other
occasions. We also note one participant (S137) that
reported workHours=0 in all occasions. Thus, responses are
interpolated based on the weekHours question from the
prelqs data of the same participant, and we flag this
participant as a potential careless respondent.
# inspecting cases of workHours = 0
cat(nrow(diary_wide[!is.na(diary_wide$workHours) & diary_wide$workHours==0,]),"cases of workHours = 0")## 21 cases of workHours = 0# inspecting responses to critical variables for such cases
diary_wide[!is.na(diary_wide$workHours) & diary_wide$workHours==0,
c("ID","dayOff_aft","dayOff_eve","dayOffyesterday","workHours","lateWorkHours")]# interpolating responses (i.e., assigning the mean No. of workHours for each participant)
IDs <- levels(as.factor(as.character(diary_wide[!is.na(diary_wide$workHours) & diary_wide$workHours==0,"ID"])))
for(ID in IDs[1:(length(IDs)-1)]){
diary_wide[diary_wide$ID==ID & !is.na(diary_wide$workHours) & diary_wide$workHours==0,"workHours"] <-
mean(diary_wide[diary_wide$ID==ID & !is.na(diary_wide$workHours) & diary_wide$workHours!=0,"workHours"])}
# interpolating responses of one participant that always reported workHours=0 based on prelqs weekHours
prelqs[prelqs$ID=="S137",c("ID","start","job","position","sector","weekHours","weekHours_remote","workTime")]diary_wide[diary_wide$ID=="S137" & !is.na(diary_wide$dayOff_eve) & diary_wide$dayOff_eve==FALSE,"workHours"] <-
prelqs[prelqs$ID=="S137","weekHours"]/5
# flagging S137 as a potential careless respondent
prelqs$careless <- FALSE
prelqs[prelqs$ID=="S137","careless"] <- TRUE
diary_wide <- plyr::join(diary_wide,prelqs[,c("ID","careless")],by="ID",type="left") # joining careless to diary_wide3.6.2. Start time
Here, we inspect the distribution of start times looking
for filtering responses out of time (e.g., morning
diary filled in the evening).
# plotting
par(mfrow=c(1,3))
hist(as.POSIXct(substr(diary_wide$start_aft,12,19),format="%H:%M:%S"),breaks="hours",main="Afternoon",xlab="")
hist(as.POSIXct(substr(diary_wide$start_eve,12,19),format="%H:%M:%S"),breaks="hours",main="Evening",xlab="")
hist(as.POSIXct(substr(diary_wide$start_mor,12,19),format="%H:%M:%S"),breaks="hours",main="Morning",xlab="")
3.6.2.1. Filtering Afternoon
With flagged Afternoon responses given after 22:00 (N = 11) :
we keep all responses that were submitted up to one hour before starting the Evening diary (N = 3)
we only consider retrospective items (i.e.,
dayOff_aft,WL,WHLSM,WE) and not momentary items (i.e., confounders and BP are removed) for responses submitted less than one hour before starting the Evening diary (N = 6)we recode momentary items (i.e., BP, confounders) as they were responded in the Evening for all responses submitted later than 22:00 with no subsequent Evening response (i.e., BP values) but with nonmissing afternoon BP (N = 2)
# selecting flagged cases - afternoon (N = 11)
diary_wide$IDday <- paste(diary_wide$ID,diary_wide$day,sep="_") # re-creating IDday
flagA <- diary_wide[!is.na(diary_wide$start_aft) & (as.POSIXct(substr(diary_wide$start_aft,12,16),format="%H:%M") < # A after 22:00
as.POSIXct("15:00",format="%H:%M",tz="GMT") |
as.POSIXct(substr(diary_wide$start_aft,12,16),format="%H:%M") >
as.POSIXct("22:00",format="%H:%M",tz="GMT")),]
flagA[,c("IDday","start_aft","end_aft","start_eve","workHours","lateWorkHours","workTime")] # showing flagged cases# marking responses
flagA$flag <- NA
flagA[!is.na(flagA$start_eve) & difftime(flagA$start_eve,flagA$end_aft,units="hours")>1,"flag"] <- "ok"
flagA[!is.na(flagA$start_eve) & difftime(flagA$start_eve,flagA$end_aft,units="hours")<=1,"flag"] <- "momentary.OUT"
flagA[is.na(flagA$SBP_eve) & !is.na(flagA$SBP_aft),"flag"] <- "toEvening"
table(flagA$flag) # showing No. of cases##
## momentary.OUT ok toEvening
## 6 3 2# filtering responses
diary_wide$flagA <- NA # flagging all flagA cases in the diary_wide dataset
diary_wide[diary_wide$IDday%in%levels(as.factor(flagA$IDday)),"flagA"] <- flagA[,"flag"]
aft_cols <- which(colnames(diary_wide)=="SBP_aft"):which(colnames(diary_wide)=="meal_aft") # afternoon momentary columns
eve_cols <- which(colnames(diary_wide)=="SBP_eve"):which(colnames(diary_wide)=="meal_eve") # evening momentary columns
diary_wide[!is.na(diary_wide$flagA) & diary_wide$flagA=="momentary.OUT",aft_cols] <- NA # removing momentary (N = 6)
diary_wide[!is.na(diary_wide$flagA) & diary_wide$flagA=="toEvening",eve_cols] <- # momentary values to Evening (N = 2)
diary_wide[!is.na(diary_wide$flagA) & diary_wide$flagA=="toEvening",aft_cols[aft_cols!=which(colnames(diary_wide)=="where_aft")]]
diary_wide[!is.na(diary_wide$flagA) & diary_wide$flagA=="toEvening",
aft_cols[aft_cols!=which(colnames(diary_wide)=="where_aft")]] <- NA # removing mom. from those cases
# sanity check
diary_wide[!is.na(diary_wide$flagA),c("flagA","end_aft","start_eve","SBP_aft","SBP_eve","WL1","EE1")]3.6.2.2. Filtering Evening
With flagged Evening responses (N = 16) given after 05:00:
we keep all responses that were submitted between 21:00 and 5:00 and up to one hour after submitting the Afternoon diary (N = 8), by assuming that (a) in a few cases participant went to sleep around 21:30 (N = 7), and (b) responses that took 1+ hours (N = 1) were open early by mistake but responded at the right time (i.e., bedtime)
we remove all momentary responses (i.e., BP, confounders) that were submitted less than one hour after submitting the Afternoon diary (N = 2)
we keep all responses submitted between 21:00 and 5:00 with missing Afternoon responses (N = 3)
we remove all responses submitted after 05:00 when followed by the morning surveys with nonmissing BP measurements (N = 2)
we recode momentary items (i.e., BP, confounders) as they were responded in the Morning, while removing retrospective items for responses submitted later than 05:00 with no subsequent Morning response (N = 1)
# selecting flagged cases - evening (N = 16)
flagE <- diary_wide[!is.na(diary_wide$start_eve) & (as.POSIXct(substr(diary_wide$start_eve,12,16),format="%H:%M") < # E after 5:00
as.POSIXct("21:30",format="%H:%M",tz="GMT") &
as.POSIXct(substr(diary_wide$start_eve,12,16),format="%H:%M") >
as.POSIXct("05:00",format="%H:%M",tz="GMT")),]
flagE$wakeTime <- substr(flagE$wakeTime,12,16)
flagE[,c("ID","end_aft","start_eve","end_eve","start_mor","wakeTime","workTime")] # showing flagged cases# marking responses
flagE$flag <- NA
flagE[!is.na(flagE$start_aft) & difftime(flagE$end_eve,flagE$end_aft,units="hours")>=1 & # >21:00 & >1h from Aft (N = 8)
as.POSIXct(substr(flagE$end_eve,12,16),format="%H:%M") > as.POSIXct("21:00",format="%H:%M",tz="GMT") |
as.POSIXct(substr(flagE$end_eve,12,16),format="%H:%M") < as.POSIXct("05:00",format="%H:%M",tz="GMT"),"flag"] <- "ok1"
flagE[!is.na(flagE$start_aft) & difftime(flagE$end_eve,flagE$end_aft,units="hours")<1,"flag"] <- "momentary.OUT" # >1h from Aft (2)
flagE[is.na(flagE$start_aft) & # >21:00 & without Aft (N = 3)
as.POSIXct(substr(flagE$end_eve,12,16),format="%H:%M") > as.POSIXct("21:00",format="%H:%M",tz="GMT") |
as.POSIXct(substr(flagE$end_eve,12,16),format="%H:%M") < as.POSIXct("05:00",format="%H:%M",tz="GMT"),"flag"] <-"ok2"
flagE[!is.na(flagE$SBP_mor) & is.na(flagE$flag) & # >5:00 & nonmissing Morning BP (N = 2)
as.POSIXct(substr(flagE$end_eve,12,16),format="%H:%M") > as.POSIXct("05:00",format="%H:%M",tz="GMT") &
as.POSIXct(substr(flagE$end_eve,12,16),format="%H:%M") < as.POSIXct("21:00",format="%H:%M",tz="GMT"),"flag"] <-"OUT"
flagE[is.na(flagE$SBP_mor) & # >5:00 & missing Morning BP (N = 1)
as.POSIXct(substr(flagE$end_eve,12,16),format="%H:%M") > as.POSIXct("05:00",format="%H:%M",tz="GMT") &
as.POSIXct(substr(flagE$end_eve,12,16),format="%H:%M") < as.POSIXct("21:00",format="%H:%M",tz="GMT"),"flag"] <-"toMorning"
table(flagE$flag) # showing No. of cases##
## momentary.OUT ok1 ok2 OUT toMorning
## 2 8 3 2 1# filtering responses
diary_wide$flagE <- NA # flagging all flagM cases in the diary_wide dataset
eve_cols.full <- which(colnames(diary_wide)=="start_eve"):which(colnames(diary_wide)=="workHours") # evening full columns
mor_cols <- which(colnames(diary_wide)=="SBP_mor"):which(colnames(diary_wide)=="meal_mor") # morning momentary columns
diary_wide[diary_wide$IDday%in%levels(as.factor(flagE$IDday)),"flagE"] <- flagE[,"flag"]
diary_wide[!is.na(diary_wide$flagE) & diary_wide$flagE=="momentary.OUT",eve_cols] <- NA # removing momentary Evening (N = 2)
diary_wide[!is.na(diary_wide$flagE) & diary_wide$flagE=="OUT",eve_cols.full] <- NA # removing all Evening (N = 2)
diary_wide[!is.na(diary_wide$flagE) & diary_wide$flagE=="OUT","eve"] <- 0
diary_wide[!is.na(diary_wide$flagE) & diary_wide$flagE=="toMorning",
mor_cols[mor_cols!=which(colnames(diary_wide)=="where_mor")]] <- # momentary values to Morning (N = 1)
diary_wide[!is.na(diary_wide$flagE) & diary_wide$flagE=="toMorning",eve_cols]
diary_wide[!is.na(diary_wide$flagE) & diary_wide$flagE=="toMorning",eve_cols] <- NA # removing mom. from those cases
# sanity check
diary_wide[!is.na(diary_wide$flagE),c("flagE","end_aft","end_eve","end_mor","SBP_eve","SBP_mor","EE1","OC1")]3.6.2.3. Filtering Morning
With flagged Morning responses (N = 23) given after 05:00:
we keep all responses started on Saturdays before 15:00 (N = 6)
we keep all responses submitted on Tuesday-Friday before 15:00 and up to three hours before submitting the Afternoon diary (N = 11)
we remove all responses submitted after 15:00 (N = 4)
we remove momentary items from responses submitted less than three hours before submitting the Afternoon diary with nonmissing BP values (N = 1)
# selecting flagged cases - morning (N = 22)
flagM <- diary_wide[!is.na(diary_wide$start_mor) & (as.POSIXct(substr(diary_wide$start_mor,12,16),format="%H:%M") < # M after 12:00
as.POSIXct("23:59",format="%H:%M",tz="GMT") &
as.POSIXct(substr(diary_wide$start_mor,12,16),format="%H:%M") >
as.POSIXct("12:00",format="%H:%M",tz="GMT")),]
# adding next day start_aft (start_aft.nd) when nonmissing
flagM$start_aft.nd <- as.POSIXct(NA)
flagM1 <- as.data.frame(matrix(nrow=0,ncol=ncol(flagM)))
for(i in 1:nrow(flagM)){ flagM1 <- rbind(flagM1,flagM[i,])
if(nrow(diary_wide[diary_wide$ID==flagM[i,"ID"] & diary_wide$day==flagM[i,"day"]+1,])>0){
flagM1[nrow(flagM1),"start_aft.nd"]<-diary_wide[diary_wide$ID==flagM[i,"ID"]&diary_wide$day==flagM[i,"day"]+1,"start_aft"]}}
# showing cases
flagM1[,c("ID","day","wakeTime","start_mor","end_mor","start_aft.nd")]# marking responses
flagM1$flag <- NA
flagM1[flagM1$day%in%c(5,11) & # Saturdays with start <15:00 (N = 6)
as.POSIXct(substr(flagM1$start_mor,12,16),format="%H:%M") < as.POSIXct("15:00",format="%H:%M",tz="GMT"),"flag"] <- "ok1"
flagM1[!flagM1$day%in%c(5,11) & difftime(flagM1$start_aft.nd,flagM1$end_mor,units="hours")>3 & # Tue-Fry <15:00 & >3h from Aft (11)
as.POSIXct(substr(flagM1$start_mor,12,16),format="%H:%M") < as.POSIXct("15:00",format="%H:%M",tz="GMT"),"flag"] <- "ok2"
flagM1[as.POSIXct(substr(flagM1$start_mor,12,16),format="%H:%M") > # submitted >15:00 (N = 4)
as.POSIXct("15:00",format="%H:%M",tz="GMT"),"flag"] <- "OUT"
flagM1[is.na(flagM1$flag),"flag"] <- "momentary.OUT" # submitted <3h before following Afternoon (N = 1)
table(flagM1$flag) # showing No. of cases##
## momentary.OUT ok1 ok2 OUT
## 1 6 11 4# filtering responses
diary_wide$flagM <- NA # flagging all flagM cases in the diary_wide dataset
mor_cols.full <- which(colnames(diary_wide)=="start_mor"):which(colnames(diary_wide)=="AWL3") # morning full columns
diary_wide[diary_wide$IDday%in%levels(as.factor(flagM1$IDday)),"flagM"] <- flagM1[,"flag"]
diary_wide[!is.na(diary_wide$flagM) & diary_wide$flagM=="momentary.OUT",mor_cols] <- NA # removing momentary Morning (N = 1)
diary_wide[!is.na(diary_wide$flagM) & diary_wide$flagM=="OUT",mor_cols.full] <- NA # removing all Morning (N = 4)
diary_wide[!is.na(diary_wide$flagM) & diary_wide$flagM=="OUT","mor"] <- 0
# sanity check
diary_wide[!is.na(diary_wide$flagM),c("flagM","start_mor","end_mor","SBP_mor","OC1")]3.6.2.4. Summary
In summary, we processed diary_wide responses by
removing those obtained out of the scheduled times and by recoding time
points based on the hour of the day. Specifically, we removed 6
cases (2 Morning, 4 Evening) with response times extremely out
of bounds, we removed momentary item responses from 9
cases (6 Afternoon, 2 Evening, 1 Morning) with BP measurements
too close to the preceding or the following ones, and we recoded
3 cases (2 Afternoon, 1 Evening) by assigning momentary item
responses to the following diary. Here, we re-inspect the distribution
of response times in the processed data.
# creating new variable summarizing flagged cases
diary_wide$flagTime <- FALSE
diary_wide[(!is.na(diary_wide$flagA) & diary_wide$flagA!="ok") |
(!is.na(diary_wide$flagE) & diary_wide$flagE!="ok1" & diary_wide$flagE!="ok2") |
(!is.na(diary_wide$flagM) & diary_wide$flagM!="ok1" & diary_wide$flagM!="ok2"),"flagTime"] <- TRUE
diary_wide$flagA <- diary_wide$flagE <- diary_wide$flagM <- NULL # removing individual flag variables
# plotting
par(mfrow=c(1,3))
hist(as.POSIXct(substr(diary_wide[!is.na(diary_wide$SBP_aft),
"start_aft"],12,19),format="%H:%M:%S"),breaks="hours",main="Aft",xlab="")
hist(as.POSIXct(substr(diary_wide[!is.na(diary_wide$SBP_eve),
"start_eve"],12,19),format="%H:%M:%S"),breaks="hours",main="Eve",xlab="")
hist(as.POSIXct(substr(diary_wide[!is.na(diary_wide$SBP_mor),
"start_mor"],12,19),format="%H:%M:%S"),breaks="hours",main="Mor",xlab="")
3.6.3. Work hours
Here, we inspect the distribution of workHours and
lateWorkHours from the diary_wide dataset, and
that of weekHours from the prelqs dataset,
looking for cases of unrealistic numbers of working hours. We also
compute the workHours_total variable as the sum of
workhours and lateWorkHours.
However, we can see that the latter variable would be inaccurate as
it might have value > 24h in some cases, implying that a number of
participants did not consider workhours and
lateWorkhours as mutually exclusive. Thus, we remove it
from the dataset.
Moreover, whereas workhours seems quite realistic
(ranging from 1h to 15h), lateWorkHours looks
inaccurately responded by a number of participants that
reported up to 10-20 hours of work after the ‘ordinary’ work schedule.
Thus, we dychotomize lateWorkHours as
FALSE (lateWorkHours = 0) or TRUE
(lateWorkHours > 0)
# computing workHours_total
diary_wide$workHours_total <- diary_wide$workHours + diary_wide$lateWorkHours
# plotting
par(mfrow=c(1,4)); hist(diary_wide$workHours,breaks=30); hist(diary_wide$lateWorkHours,breaks=30)
hist(diary_wide$workHours_total,breaks=30); hist(prelqs$weekHours,breaks=30)
# removing workHours_total
diary_wide$workHours_total <- NULL
# cases of lateWorkHours > 10
diary_wide[!is.na(diary_wide$lateWorkHours) & diary_wide$lateWorkHours>10,c("ID","day","workHours","lateWorkHours",
"SQ1","wakeTime","workTime","weekHours",
"weekHours_remote","job")]# dychotomizing lateWorkHours
diary_wide$lateWorkHours1 <- NA
diary_wide[!is.na(diary_wide$lateWorkHours) & diary_wide$lateWorkHours==0,"lateWorkHours1"] <- FALSE
diary_wide[!is.na(diary_wide$lateWorkHours) & diary_wide$lateWorkHours>0,"lateWorkHours1"] <- TRUE
diary_wide$lateWorkHours <- diary_wide$lateWorkHours1
diary_wide$lateWorkHours1 <- NULL
summary(diary_wide$lateWorkHours) # summarizing cases FALSE=702, TRUE=245, NA=197## Mode FALSE TRUE NA's
## logical 702 245 1973.6.4. BP confounders
Finally, we observe a number of cases (N = 2-7) with nonmissing
response to the core diary items (e.g., workaholism,
exhaustion, and sleep items) but missing response to the blood pressure
confounders. Here, we set the confounders = FALSE for all
of these cases.
# afternoon
cat(nrow(diary_wide[!is.na(diary_wide$WHLSM1) & is.na(diary_wide$confounders_aft),]),
"cases with missing confounders_aft but nonmissing WHLSM1 item score")## 7 cases with missing confounders_aft but nonmissing WHLSM1 item scorediary_wide[!is.na(diary_wide$WHLSM1) & is.na(diary_wide$confounders_aft),"confounders_aft"] <- FALSE
# evening
cat(nrow(diary_wide[!is.na(diary_wide$EE1) & is.na(diary_wide$confounders_eve),]),
"cases with missing confounders_eve but nonmissing EVE1 item score")## 2 cases with missing confounders_eve but nonmissing EVE1 item scorediary_wide[!is.na(diary_wide$EE1) & is.na(diary_wide$confounders_eve),"confounders_eve"] <- FALSE
# same thing for flagBP variables
diary_wide[!is.na(diary_wide$WHLSM_1) & is.na(diary$flagBP_aft),"flagBP_aft"] <- FALSE
diary_wide[!is.na(diary_wide$EE_1) & is.na(diary$flagBP_eve),"flagBP_eve"] <- FALSE4. Data selection
Here, we select the observations, participants, and variables to be considered for the subsequent analyses. That is, we discard pilot participants and all related observations, and we remove the variables not considered in the present work.
4.1. Variable selection
Here, we subset the data by selecting and sorting the variables to be considered in the following analyses, whereas unused variables are not included.
# diary data # case identification
diary_wide <- diary_wide[,c("ID","day",
# compliance
"aft","eve","mor",
# data quality
"flagTime","flagBP_aft","flagBP_eve","careless",
# afternoon diary
"start_aft","end_aft", # starting & ending date & time
"dayOff_aft", # took a day off (missing responses)
"SBP_aft","DBP_aft", # blood pressure
"where_aft","confounders_aft","coffee_aft","smoke_aft","sport_aft","meal_aft", # confounders
paste("WHLSM",1:6,sep=""), # state workaholism
# evening diary
"start_eve","end_eve", # starting & ending date & time
"dayOff_eve", # took a day off (missing responses)
"SBP_eve","DBP_eve", # blood pressure
"confounders_eve","coffee_eve","smoke_eve","sport_eve","meal_eve", # confounders
"teleWork","workHours","dailyHassles_eve", # further confounders/covariates
paste("EE",1:4,sep=""), # emotional exhaustion
paste0("R.det",1:3), # psychological detachment
# morning diary
"start_mor","end_mor", # starting & ending date & time
"dayOffyesterday", # took a day off yesterday or today (missing responses)
"lateWorkHours","wakeTime","hhFromAwake", # confounders/covariates
paste("SQ",1:4,sep=""), # sleep quality
# demographics from the preqls dataset
colnames(diary_wide)[which(colnames(diary_wide)=="gender"):which(colnames(diary_wide)=="weekHours")],
# Retrospective workaholism items from the prelqs dataset
paste0("duwas",1:10))]4.2. Case selection
Here, we subset the data by selecting the cases to be considered in the following analyses. That is, we filter 12 pilot participants that took part in the pilot trial of the study (i.e., before 2021-04-05).
# selecting pilot participants (12)
(pilots <- levels(as.factor(as.character(prelqs[prelqs$start<as.POSIXct("2021-04-05"),"ID"]))))## [1] "S005" "S013" "S067" "S074" "S089" "S094" "S112" "S121" "S133" "S134"
## [11] "S139" "S141"# filtering 12 pilot participants from diary data
memory <- diary
memory2 <- diary_wide
diary <- diary[!(diary$ID%in%pilots),]
diary_wide <- diary_wide[!(diary_wide$ID%in%pilots),]
diary$ID <- as.factor(as.character(diary$ID))
diary_wide$ID <- as.factor(as.character(diary_wide$ID))
cat("Removed",nlevels(memory$ID)-nlevels(diary$ID),"participants,",
nrow(memory2)-nrow(diary_wide),"days,",nrow(memory)-nrow(diary),"observations")## Removed 12 participants, 60 days, 149 observationscat("diary_wide: total No. of responses =",nrow(diary_wide),"from",nlevels(diary_wide$ID),"participants")## diary_wide: total No. of responses = 1084 from 135 participants5. Data dictionary
Here, we provide a definition for each variable in the processed
diary_wide datasets.
str(diary_wide)## 'data.frame': 1084 obs. of 86 variables:
## $ ID : Factor w/ 135 levels "S001","S002",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ day : num 1 2 3 4 5 7 8 9 10 11 ...
## $ aft : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
## $ eve : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
## $ mor : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
## $ flagTime : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ flagBP_aft : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ flagBP_eve : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ careless : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ start_aft : POSIXct, format: "2022-01-24 15:42:17" "2022-01-25 15:50:20" ...
## $ end_aft : POSIXct, format: "2022-01-24 15:45:52" "2022-01-25 15:52:14" ...
## $ dayOff_aft : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ SBP_aft : num 130 114 114 120 112 ...
## $ DBP_aft : num 73 75 75.5 78.5 75.5 82 76 83.5 75.5 80 ...
## $ where_aft : Factor w/ 3 levels "home","workplace",..: 1 1 1 1 1 1 1 1 2 1 ...
## $ confounders_aft : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ coffee_aft : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ smoke_aft : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ sport_aft : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ meal_aft : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ WHLSM1 : num 3 6 5 3 3 3 2 3 2 3 ...
## $ WHLSM2 : num 2 6 6 3 2 3 2 3 2 4 ...
## $ WHLSM3 : num 1 6 1 2 1 1 1 1 3 1 ...
## $ WHLSM4 : num 2 4 2 1 3 3 2 3 2 3 ...
## $ WHLSM5 : num 1 2 1 1 1 1 1 1 2 1 ...
## $ WHLSM6 : num 1 3 5 2 3 4 2 5 2 5 ...
## $ start_eve : POSIXct, format: "2022-01-24 21:50:17" "2022-01-25 22:17:12" ...
## $ end_eve : POSIXct, format: "2022-01-24 21:53:33" "2022-01-25 22:20:13" ...
## $ dayOff_eve : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ SBP_eve : num 122 108 106 113 112 ...
## $ DBP_eve : num 77 76 66.5 75 74 75.5 80 76 86.5 84 ...
## $ confounders_eve : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ coffee_eve : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ smoke_eve : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ sport_eve : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ meal_eve : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ teleWork : Factor w/ 4 levels "office","teleWork",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ workHours : num 6 6 6 6 6 6 6 6 9 6 ...
## $ dailyHassles_eve: Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ EE1 : num 5 6 7 4 3 5 5 5 5 5 ...
## $ EE2 : num 5 4 5 3 2 5 4 5 5 4 ...
## $ EE3 : num 4 1 2 2 2 5 3 1 3 1 ...
## $ EE4 : num 2 1 1 2 2 5 1 1 4 1 ...
## $ R.det1 : num 3 2 2 1 1 2 2 4 3 4 ...
## $ R.det2 : num 2 1 3 1 1 2 3 4 3 3 ...
## $ R.det3 : num 6 4 5 1 1 2 4 5 5 6 ...
## $ start_mor : POSIXct, format: "2022-01-25 08:03:06" "2022-01-26 08:01:20" ...
## $ end_mor : POSIXct, format: "2022-01-25 08:05:29" "2022-01-26 08:03:11" ...
## $ dayOffyesterday : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ lateWorkHours : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ wakeTime : POSIXct, format: "2024-05-17 06:45:00" "2024-05-17 06:45:00" ...
## $ hhFromAwake : num 1.3 1.27 1.27 1.68 1.37 ...
## $ SQ1 : num 2 1 1 3 2 2 2 1 2 2 ...
## $ SQ2 : num 3 1 1 3 1 1 2 1 2 1 ...
## $ SQ3 : num 1 1 1 3 1 1 1 1 2 2 ...
## $ SQ4 : num 1 1 1 2 1 1 1 1 2 1 ...
## $ gender : Factor w/ 2 levels "F","M": 1 1 1 1 1 1 1 1 1 1 ...
## $ age : int 59 59 59 59 59 59 59 59 59 59 ...
## $ BMI : num 18.8 18.8 18.8 18.8 18.8 ...
## $ edu : Factor w/ 2 levels "highschool-",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ mStatus : Factor w/ 4 levels "single","partner",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ home : Factor w/ 5 levels "alone","partner",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ children : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 2 2 2 2 ...
## $ home_child : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 2 2 2 2 ...
## $ partner : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 2 2 2 2 ...
## $ home_partner : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ smoker : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 2 2 2 2 ...
## $ bp_drugs : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ horm_drugs : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ psy_drugs : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ cv_dysf : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ sleep_dysf : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ job : Factor w/ 22 levels "Administrative and commercial managers",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ position : Factor w/ 3 levels "Employee","Manager/Employers",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ sector : Factor w/ 2 levels "Private","Public": 1 1 1 1 1 1 1 1 1 1 ...
## $ weekHours : num 35 35 35 35 35 35 35 35 35 35 ...
## $ duwas1 : int 2 2 2 2 2 2 2 2 2 2 ...
## $ duwas2 : int 4 4 4 4 4 4 4 4 4 4 ...
## $ duwas3 : int 2 2 2 2 2 2 2 2 2 2 ...
## $ duwas4 : int 4 4 4 4 4 4 4 4 4 4 ...
## $ duwas5 : int 2 2 2 2 2 2 2 2 2 2 ...
## $ duwas6 : int 2 2 2 2 2 2 2 2 2 2 ...
## $ duwas7 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ duwas8 : int 2 2 2 2 2 2 2 2 2 2 ...
## $ duwas9 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ duwas10 : int 3 3 3 3 3 3 3 3 3 3 ...Identification
ID= participant’s identification codeday= day of participation (from 1 to 11)
Compliance
aft= day including the response to the Afternoon questionnaire (1) or not (0)eve= day including the response to the Evening questionnaire (1) or not (0)mor= day including the response to the Morning questionnaire (1) or not (0)
Data quality
flagTime= responses recoded due to wrong response timing (i.e., responses given outside the scheduled intervals) (see section 3.6.2)flagBP_aft-flagBP_eve= flagged cases that were reprocessed due to extreme BP values (see section 3.2)careless= participant flagged as a careless respondent (careless = TRUE) due to inconsistent responses in theDayOffvariables (see section 3.4.1) - the same variable is included in theprelqsdataset
Afternoon questionnaire
start_aft= starting time of the Afternoon questionnaire (yyyy-mm-dd hh:mm:ss)end_aft= submission time of the Afternoon questionnaire (yyyy-mm-dd hh:mm:ss)dayOff_aft= logical variable indicating whether the participant reported working on that day (FALSE) or not (TRUE)SBP_aft-DBP_aft= systolic and diastolic aggregate blood pressure value (mmHg) measured in the Afternoonwhere_aft= place where the Afternoon blood pressure recording was done (“home”, “workplace”, “other”)confounders_aft= logical variable indicating the presence (TRUE) or absence (FALSE) of any confounder before the Afternoon recordingcoffee_aft-meal_aft= variables indicating the presence (1) or absence (0) of each confounder (i.e., cofee, smoke, sport, and meal)WHLSM1-WHLSM_6= item scores at the diary version of the DUWAS (workaholism) (1-7)
Evening questionnaire
start_eve= starting time of the Evening questionnaire (yyyy-mm-dd hh:mm:ss)end_eve= submission time of the Evening questionnaire (yyyy-mm-dd hh:mm:ss)dayOff_eve= logical variable indicating whether the participant reported working on that day (FALSE) or not (TRUE)SBP_eve-DBP_eve= systolic and diastolic aggregate blood pressure value (mmHg) measured in the Eveningconfounders_eve= logical variable indicating the presence (TRUE) or absence (FALSE) of any confounder before the Evening recordingcoffee_eve-meal_eve= variables indicating the presence (1) or absence (0) of each confounder (i.e., cofee, smoke, sport, and meal)teleWork= factor indicating whether on that day the participant worked in the “office”, did “teleWork”, or “both”workHours= number of working hours for that day (No.)dailyHassless_eve= factor indicating whether the participant reported some daily hassles outside the working time on that day (“Yes”) or not (“No”)EE1-EE4= item scores at the diary version of the Copenhagen Burnout Inventory (emotional exhaustion) (1-7)R.det1-R.det3= item scores at the diary version of the Recovery Experiences Questionnaire (psychological detachment) (1-7)
Morning questionnaire
start_mor= starting time of the Morning questionnaire (yyyy-mm-dd hh:mm:ss)end_mor= submission time of the Morning questionnaire (yyyy-mm-dd hh:mm:ss)dayOffyesterday= logical variable indicating whether the participant reported working on the previous day day (FALSE) or not (TRUE)lateWorkHours= logical variable indicating whether the participant reported working in the previous evening (TRUE) or not (FALSE)wakeTime= self-reported waking time (yyyy-mm-dd hh:mm:ss)hhFromAwake= number of hours between waketime and the response to the Morning questionnaireSQ-SQ4= item scores at the diary version of the Mini Sleep Questionnaire (sleep disturbances) (1-7)
Retrospective time-invariant variables (measured with the preliminary questionnaire)
Demographics
gender= participant’s gender (“F” or “M”)age= participant’s age (years)BMI= participant’s body mass index (kg/m^2)edu= participant’s education level (“middle”, “highschool”, “university+”)mStatus= participant’s marital status (“single”, “partner”, “divorced”, “widowed”)home= family situation (living “alone” or with “partner”, “children”, “parents”, “others”)children= number of children (No.)home_child= living with children (Yes/No)partner= having a partner (Yes/No)home_partner= living with partner (Yes/No)
Confounders and inclusion criteria
smoker= smoking status (“No”, “Yes”, “Quit_less”, “Quit_more”)bp_drugs= participant reporting taking blood pressure medications (e.g., diuretics, beta-blokkants, anti-hypertension)horm_drugs= participant reporting taking hormonal medications (e.g., birth control)psy_drugs= participant reporting taking psychiatric drugs (e.g., antidepressants, anxiety)cv_dysf= participant reporting suffering from a cardiovascular disease (e.g., hypertension, ischemia, strokes)sleep_dysf= participant reporting suffering from a sleep-related disease (e.g., insomnia, parasomnia, sleep apnea)
Occupational variables
job= participant’s job recoded using the ISCO-08 classification of occupations (level 2) (Ganzeboom, 2010position= participant’s job position (“Employee”, “Project”, “Manager”, “(Self-)Employer”)sector= participant’s job sector (“Private” or “Public”)weekHours= participant’s self-reported mean number of working hours per week (No.)
6. Data export
Here, we export the recoded and pre-processed diary_wide
dataset (renamed as diary) to be used for further analyses.
The datasets is exported in both CSV and RData format.
# exporting diary data
diary <- diary_wide # renamed as 'diary'
save(diary,file="DATI/diary_processed.RData") # RData
write.csv2(diary,file="DATI/diary_processed.csv", row.names=FALSE) # csv with ";"References
- Ganzeboom, H. B. (2010). International standard classification of occupations ISCO-08 with ISEI-08 scores. Version of July, 27, 2010. Available from this link.
R packages
Auguie, Baptiste. 2017. gridExtra: Miscellaneous Functions for
"Grid" Graphics. https://CRAN.R-project.org/package=gridExtra.
Bache, Stefan Milton, and Hadley Wickham. 2022. Magrittr: A
Forward-Pipe Operator for r. https://CRAN.R-project.org/package=magrittr.
Dowle, Matt, and Arun Srinivasan. 2023. Data.table: Extension of
‘Data.frame‘. https://CRAN.R-project.org/package=data.table.
Grolemund, Garrett, and Hadley Wickham. 2011. “Dates and Times
Made Easy with lubridate.”
Journal of Statistical Software 40 (3): 1–25. https://www.jstatsoft.org/v40/i03/.
Kouretsis, Alexandros, Andreas Bampouris, Petros Morfiris, and
Konstantinos Papageorgiou. 2020. labourR: Classify Multilingual
Labour Market Free-Text to Standardized Hierarchical Occupations.
https://github.com/AleKoure/labourR.
R Core Team. 2023. R: A Language and Environment for Statistical
Computing. Vienna, Austria: R Foundation for Statistical Computing.
https://www.R-project.org/.
Spinu, Vitalie, Garrett Grolemund, and Hadley Wickham. 2023.
Lubridate: Make Dealing with Dates a Little Easier. https://CRAN.R-project.org/package=lubridate.
Wickham, Hadley. 2011. “The Split-Apply-Combine Strategy for Data
Analysis.” Journal of Statistical Software 40 (1): 1–29.
https://www.jstatsoft.org/v40/i01/.
———. 2016. Ggplot2: Elegant Graphics for Data Analysis.
Springer-Verlag New York. https://ggplot2.tidyverse.org.
———. 2022. Plyr: Tools for Splitting, Applying and Combining
Data. https://CRAN.R-project.org/package=plyr.
Wickham, Hadley, Winston Chang, Lionel Henry, Thomas Lin Pedersen,
Kohske Takahashi, Claus Wilke, Kara Woo, Hiroaki Yutani, and Dewey
Dunnington. 2023. Ggplot2: Create Elegant Data Visualisations Using
the Grammar of Graphics. https://CRAN.R-project.org/package=ggplot2.
---
title: "The daily costs of workaholism"
subtitle: "Supplementary material S3: Data pre-processing"
author:  "Luca Menghini, Ph.D., Cristian Balducci, Ph.D."
date: "`r Sys.Date()`"
bibliography: [packagesProc.bib]
nocite: '@*'
output:
  html_document:
    df_print: paged
    toc: true
    toc_float: true
    toc_depth: 4
    css: styles.css
    code_download: true
  pdf_document: default
  word_document: default
  theme: united
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

<br>

# Aims and content

The present document includes the pre-processing steps to read the intensive longitudinal data collected with the Qualtrics platform (Qualtrics, Seattle, WA, USA) and prepare them from the analyses. The data were collected from a sample of full-time workers (mainly office
workers) that responded to a preliminary questionnaire `prelqs` followed by a one- or two-week daily diary protocol `diary`.

Here, we remove all objects from the R global environment, and we set the system time zone.

``` {r}
# removing all objets from the workspace
rm(list=ls())

# setting system time zone to GMT (for consistent temporal synchronization)
Sys.setenv(tz="GMT")
```

The following R packages are used in this document (see [References](#ref) section):

``` {r}
# required packages
packages <- c("lubridate","ggplot2","gridExtra","plyr","labourR","data.table","magrittr")

# generate packages references
knitr::write_bib(c(.packages(), packages),"packagesProc.bib")

# # run to install missing packages
# xfun::pkg_attach2(packages, message = FALSE); rm(list=ls())
```

<br>

# 1. Data reading

First, we read **preliminary questionnaire** `prelqs` data exported from Qualtrics (available upon request to the main author).

``` {r}
# loading preliminary questionnaire dataset (skipping first two lines including unuseful information))
prelqs <- read.csv("DATI/WHLSM2021 - prelQS.csv", 
                   na.strings="")[3:nrow(read.csv("DATI/WHLSM2021 - prelQS.csv")),]

# removing 1 case with missing values to all items
prelqs <- prelqs[!is.na(prelqs$gender),]

# removing 1 case with no e-mail address (i.e., no identification code)
prelqs <- prelqs[!is.na(prelqs$RecipientEmail),]
```

<br>

Second, we read the `diary` datasets exported from Qualtrics as CSV files with numeric values (available upon request to the main author). A separate diary survey (resulting in a separate dataset) was used for afternoon `_a`, evening `_e`, and morning `_m` diaries, respectively. Moreover, a separate survey was used for Saturday morning (`_m_sat`), and surveys sometimes (i.e., in case of techical problems with the smartphones), they administered by sending anonymized links including an e-mail field: `_mail`.

``` {r}
# loading diary datasets
diary_a <- read.csv("DATI/WEEK40/WHLSM2021_DiaryAfternoon.csv",
                    na.strings="")[3:nrow(read.csv("DATI/WEEK40/WHLSM2021_DiaryAfternoon.csv")),] # diary_a = Afternoon (after work)
diary_e <- read.csv("DATI/WEEK40/WHLSM2021_DiaryEvening.csv",
                    na.strings="")[3:nrow(read.csv("DATI/WEEK40/WHLSM2021_DiaryEvening.csv")),] # diary_e = Evening (bedtime)
diary_m <- read.csv("DATI/WEEK40/WHLSM2021_DiaryMorning.csv", 
                    na.strings="")[3:nrow(read.csv("DATI/WEEK40/WHLSM2021_DiaryMorning.csv")),] # diary_m = Morning (before work)
diary_m_sat <- read.csv("DATI/WEEK40/WHLSM2021_DiaryMorning_sat.csv",
                    na.strings="")[3:nrow(read.csv("DATI/WEEK40/WHLSM2021_DiaryMorning_sat.csv")),] # diary_m_sat = Saturday Morning

# loading diary datasets with e-mail field (used in cases of technical problems)
diary_m_mail <- read.csv("DATI/WEEK40/WHLSM2021_DiaryMorning_mail.csv",
                         na.strings="")[3:nrow(read.csv("DATI/WEEK40/WHLSM2021_DiaryMorning_mail.csv")),]
diary_a_mail <- read.csv("DATI/WEEK40/WHLSM2021_DiaryAfternoon_mail.csv",
                         na.strings="")[3:nrow(read.csv("DATI/WEEK40/WHLSM2021_DiaryAfternoon_mail.csv")),]
diary_e_mail <- read.csv("DATI/WEEK40/WHLSM2021_DiaryEvening_mail.csv",
                         na.strings="")[3:nrow(read.csv("DATI/WEEK40/WHLSM2021_DiaryEvening_mail.csv")),]
diary_m_sat_mail <- read.csv("DATI/WEEK40/WHLSM2021_DiaryMorning_sat_mail.csv",
                             na.strings="")[3:nrow(read.csv("DATI/WEEK40/WHLSM2021_DiaryMorning_sat_mail.csv")),]
```

<br>

# 2. Data recoding

Second, we recode the two datasets by removing unuseful columns, by re-setting variable labels and classes, by renaming relevant columns, and by merging the `diary` datasets.

## 2.1. Preliminary questionnaire

Here, we remove the unuseful columns, and we rename and sort the relevant columns of the `prelqs` dataset.
``` {r}
# removing unuseful columns
prelqs[,c("Status","IPAddress","Progress","Duration..in.seconds.","Finished","RecordedDate","ResponseId",
          "RecipientLastName","RecipientFirstName","LocationLatitude","LocationLongitude",
          "DistributionChannel","UserLanguage","consent")] <- NULL

# renaming variables
colnames(prelqs)[1:4] <- c("start","end","ID","recruiter.ID") # participant's identifier (e-mail)
colnames(prelqs)[1:which(colnames(prelqs)=="children_1")] <- # removing question numbers from column names
  gsub("_4","",gsub("_1","",colnames(prelqs)[1:which(colnames(prelqs)=="children_1")]))
colnames(prelqs)[which(substr(colnames(prelqs),1,2)=="EX")] <- paste("EX",1:7,sep="_") # recoding question numbers for EX scale
```

<br>

Then, we change the classes of the relevant variables by converting dates as `POSIXct` (the R format for dates and times), categorical variables as `factor`, and quantitative variables as `integer` or `numeric`.
``` {r}
# time (POSIXct variables): start, end
prelqs$start <- as.POSIXct(prelqs$start)
prelqs$end <- as.POSIXct(prelqs$end)

# integer: age, WHLSM, WA, OC, WE, EX, WL, WFC, OWC, TS, NA
prelqs[,c(which(colnames(prelqs)=="age"),which(colnames(prelqs)=="WHLSM_1"):which(colnames(prelqs)=="NA_4"))] <-
  lapply(prelqs[,c(which(colnames(prelqs)=="age"),which(colnames(prelqs)=="WHLSM_1"):which(colnames(prelqs)=="NA_4"))],as.integer)

# numeric: weekHours, weekHours_remote
colnames(prelqs)[which(substr(colnames(prelqs),1,4)=="week")] <- c("weekHours","weekHours_remote")
prelqs[,c("weight","height","weekHours","weekHours_remote")] <-
  lapply(prelqs[,c("weight","height","weekHours","weekHours_remote")],as.numeric)

# factors: all other variables
prelqs[,c("recruiter.ID","gender","edu","mStatus","home","children","smoker","drugs_1","drugs_2","drugs_3",
          "disfunctions_1","disfunctions_2","position","sector")] <-
  lapply(prelqs[,c("recruiter.ID","gender","edu","mStatus","home","children","smoker","drugs_1","drugs_2","drugs_3",
                   "disfunctions_1","disfunctions_2","position","sector")],as.factor)
```

<br>

We also recode the levels and values of sociodemographic variables as we preregistered (see [preregistration here](https://osf.io/h9zvq)).
``` {r}
# gender: F, M, (Other)
prelqs$gender <- as.factor(gsub("1","M",gsub("2","F",gsub("3","Other",prelqs$gender))))
if(nlevels(prelqs$gender)==2){ cat("Recoding as M/F since nobody chose gender='Other'") 
  prelqs$gender <- factor(prelqs$gender,levels=c("F","M")) }

# education: highschool or lower vs. university or higher
prelqs$edu <- as.factor(gsub("1","highschool-",gsub("2","highschool-",gsub("3","university+",prelqs$edu))))

# children: yes/no
prelqs[prelqs$children!=0,"children"] <- 1
prelqs$children <- as.factor(gsub("0","No",gsub("1","Yes",prelqs$children))) # having a children

# home situation: alone, partner, children, parents, others
prelqs$home <- gsub("1","partner",gsub("2","parents",gsub("3","children",gsub("4","alone",gsub("5","others",prelqs$home)))))
prelqs$home <- factor(gsub("partner,alone","partner", # partner and alone = partner
                           gsub("partner,others","partner", # partner and others = partner
                                gsub("partner,children","children", # if children = children
                                     gsub("alone,others","others", # alone and others = others
                                          gsub("partner,parents","partner",prelqs$home))))), # partner and parents = partner
                      levels=c("alone","partner","children","parents","others")) 

# children in the household: yes/no
prelqs$home_child <- as.factor(gsub("alone","No", # 
                                   gsub("partner","No",
                                        gsub("children","Yes",
                                             gsub("parents","No",
                                                  gsub("others","No",prelqs$home))))))

# marital status: single, partner, divorced, widowed
prelqs$mStatus <- factor(gsub("1","single",gsub("2","partner",gsub("3","divorced",gsub("4","widowed",prelqs$mStatus)))),
                         levels=c("single","partner","divorced","widowed"))

# having a partner
prelqs$partner <- as.factor(gsub("single","No",
                                gsub("partner","Yes",
                                     gsub("divorced","No",
                                          gsub("widowed","No",prelqs$mStatus))))) 

# living with partner
prelqs$home_partner <- as.factor(gsub("alone","No", # living with partner
                                     gsub("partner","Yes",
                                          gsub("children","No",
                                               gsub("parents","No",
                                                    gsub("others","No",prelqs$home))))))

# smoking status: Yes/No
prelqs$smoker <- factor(gsub("1","Yes",gsub("2","Quit_less",gsub("3","Quit_more",gsub("4","No",prelqs$smoker)))),
                        levels=c("No","Yes","Quit_less","Quit_more"))
prelqs$smoker <- as.factor(gsub("Quit_less","Yes",gsub("Quit_more","No",prelqs$smoker)))

# drugs/medications
colnames(prelqs)[which(substr(colnames(prelqs),1,5)=="drugs")] <- c("bp_drugs","horm_drugs","psy_drugs") # drugs/medications
prelqs$bp_drugs <- as.factor(gsub("1","Yes",gsub("2","No",prelqs$bp_drugs)))
prelqs$horm_drugs <- as.factor(gsub("1","Yes",gsub("2","No",prelqs$horm_drugs)))
prelqs$psy_drugs <- as.factor(gsub("1","Yes",gsub("2","No",prelqs$psy_drugs)))

# cardiovascular and sleep dysfunctions
colnames(prelqs)[which(substr(colnames(prelqs),1,5)=="disfu")] <- c("cv_dysf","sleep_dysf")
prelqs$cv_dysf <- as.factor(gsub("1","Yes",gsub("2","No",prelqs$cv_dysf)))
prelqs$sleep_dysf <- as.factor(gsub("1","Yes",gsub("2","No",prelqs$sleep_dysf)))

# job position: employee or project vs. manager or self-employed/employer
prelqs$position <- factor(gsub("1","Manager/Employers",
                               gsub("2","Employee",
                                    gsub("3","Manager/Employers",
                                         gsub("4","Project",
                                              prelqs$position)))),
                          levels=c("Employee","Manager/Employers","Project"))

# job sector: public vs. private
prelqs$sector <- as.factor(gsub("1","Public",gsub("2","Private",prelqs$sector)))

# remotePlace (open-ended question -> making it categorical)
prelqs$remotePlace <- factor(gsub("casa","home",
                                  gsub("acasa","casa",
                                       gsub("dacasa","casa",
                                            gsub("acasa/inco-wo","casa",
                                                 gsub("studioausl","studio",
                                                      gsub("ufficio","studio",
                                                           gsub("ospedale","studio",
                                                                gsub("officina","studio",
                                                                     gsub("instudioinvideocall","studio",
                                                                          gsub("nessuno","0",
                                                                               gsub("nessuni","0",
                                            gsub("\\.","",gsub(" ","",tolower(prelqs$remotePlace)))))))))))))),
                             levels=c("0","home","studio"))

# from height and weight to BMI
prelqs[prelqs$height<3,"height"] <- prelqs[prelqs$height<3,"height"]*100 # correcting heights reported in meters
prelqs$BMI <- prelqs$weight/(prelqs$height/100)^2 # BMI = kg/m^2
prelqs <- prelqs[,c(1:which(colnames(prelqs)=="age"), # removing height and weight
                    ncol(prelqs),which(colnames(prelqs)=="edu"):(ncol(prelqs)-1))]
```

<br>

### 2.1.1. Recoding jobs

Here, the `job.recode()` function is used to recode the open-ended `job` item responses by using the **ISCO-08 classification of occupations (level 2)** ([Ganzeboom, 2010](#ref)). The function is based on the `labouR` package integrated with some manual recoding. The open-ended responses are then removed for privacy reasons.

<details><summary>`job.recode()`</summary>
<p>
``` {r}
job.recode <- function(data){ require(labourR); require(data.table); require(magrittr)

  # creating corpus data
  corpus <- data.table(ID=data$ID,text=data$job,edu=data$edu,pos=data$position,lang="it")
  languages <- unique(corpus$language) # language classes
  
  # first screening based on the labourR::classify_occupation() function
  suggestions <- classify_occupation(corpus=corpus,id_col="ID",text_col="text",lang="it",isco_level=2,num_leaves=10)
  corpus <- plyr::join(corpus,suggestions,by="ID",type="left")
  
  # adjusting automatic classification based on manual screening
  corpus[corpus$text%in%c("Responsabile sistemi informativi",
                          "Responsabile di un piccolo museo e operatrice museale e teatrale nelle scuole"),
         "preferredLabel"] <- 
    "Legal, social and cultural professionals"
  corpus[corpus$text%in%c("Funzionario Tecnico pubblica amministrazione "),
         "preferredLabel"] <- 
    "Legal, social and cultural professionals"
  corpus[corpus$text%in%c("Banca - Responsabile Organizzazione",
                          "addetta amministrazione e amministratore di azienda in proprio",
                          "Commercialista"),
         "preferredLabel"] <- 
    "Business and administration professionals"
  corpus[corpus$text%in%c("Lavoro nella segreteria di una Scuola Secondaria di Primo Grado come amministrativa. Mi occupo di questioni burocratiche e organizzative, gestisco soprattutto circolari e domande di iscrizione, nulla osta, ecc. Al momento gestisco anche il controllo dei Green Pass e le varie procedure da seguire.",
                          "Attività amministrativa di ufficio e front Office ",
                          "impiegata commerciale",
                          "impiegata nel settore amministrativo",
                          "Addetta back office presso agenzia di somministrazione"),
         "preferredLabel"] <- 
    "Business and administration associate professionals"
  corpus[corpus$text%in%c("direzione piccola azienda",
                          "Dirigente d'azienda ",
                          "Dirigente multinazionale digital marketing",
                          "Founder brand moda ",
                          "Imprenditore nel settore degli alberghi, gestione di un hotel di 45 camere più sala ristorante. Gestione di 9 dipendenti, rapporti con fornitori e piattaforme online. Gestione della contabilità. Occasionalmente docente presso Cescot Rimini, corsi di formazione.",
                          "Imprenditore di carpenteria metallica ",
                          "Imprenditrice"),
         "preferredLabel"] <- 
    "Chief executives, senior officials and legislators"
  corpus[corpus$text%in%c("attività di ricerca presso università",
                          "Dottorando di Ricerca",
                          "Dottorato di ricerca",
                          "Sono un geometra contabile. Il mio lavoro consiste nel tenere traccia di tutte le lavorazioni che svolgono gli operai sui vari cantieri, dopodiché realizzo dei report fotografici e contabili per il mio datore di lavoro. \nGli operai mi inviano giornalmente le foto per realizzare i report, dalle foto ne ricavo le misure delle lavorazioni eseguite. In base alle misurazioni effettuate eseguo dei disegni contabili e realizzo un computo metrico dove controllo i costi delle lavorazioni eseguite e verifico che non superiamo il budget prefissato.",
                          "Artigiano idraulico elettricista",
                          "Geometra libero professionista"),
         "preferredLabel"] <- 
    "Science and engineering associate professionals"
  corpus[corpus$text%in%c("radiologo, analisi immagini alla workstation (computer) alternato a periodi di studio al computer e esecuzione di ecografie ",
                          "riabilitazione della voce e della deglutizione per adulti operati delle corde vocali",
                          "Psicoterapeuta presso il mio studio privato. L'attività che svolgo si rivolge ad adulti e adolescenti, quasi esclusivamente in presenza, in sedute di circa 1 h ciascuna. A ciò si aggiunge il lavoro di fatturazione, formazione e aggiornamento, gestione pratica dello studio (Back office)",
                          "infermiere coordinatore",
                          "Psichiatra psicoterapeuta "),
         "preferredLabel"] <- 
    "Health professionals"
  corpus[corpus$text%in%c("Odontotecnico da libero professionista, ",
                          "operatore socio sanitario",
                          "Tutor ABA: mi occupo dell'applicazione di procedure e strategie comportamentali per bambini con Disturbo dello spettro autistico, all'interno di un rapporto 1:1.\nDurante la sessione di lavoro è necessario costruire una buona relazione con il bambino con spettro autistico e la sua famiglia; prendere a mano note per ciascuna sessione, inserire i dati in un database; osservare il comportamento del bambino e quindi applicare una strategia idonea; monitorare gli esiti dell’intervento da condividere successivamente con il supervisore ABA, nell'ottica di una riformulazione della programmazione da implementare per quel bambino.\n"),
         "preferredLabel"] <- 
    "Health associate professionals"
  corpus[corpus$text%in%c("Coordinatore unità operativa in un servizio di  integrazione socio sanitaria in salute mentale. Coordinatore unità operativa per soggetti svantaggiati e disabili nel cooperativismo. Consigliere CDA in Cooperativa. Cultore della materia in facoltà universitaria.",
                          "Psicologa infantile, Progettista sociale, Coordinatrice Centro Famiglie",
                          "Traduttrice, addetta stampa, content creator",
                          "project manager e consulente aziendale rispetto alla psicologia clinica, psicologia organizzazioni e psicologia emergeza"),
         "preferredLabel"] <- 
    "Legal, social and cultural professionals"
  corpus[corpus$text%in%c("Energy Back Office Specialist (Addetto al Billing Gas)",
                          "Solgo 2 lavori:\nLibera professione, svolgo attività di impresa nelle società con mio fratello nel campo architettura, immobiliare e turistico \nResponsabile ricerca e sviluppo (con orari flessibili) x un quartiere fieristico"),
         "preferredLabel"] <- 
    "Science and engineering professionals"
  corpus[corpus$text%in%c("Doposcuola ",
                          "Educatrice all'interno di una scuora superiore."),
         "preferredLabel"] <- 
    "Teaching professionals"
  corpus[corpus$text%in%c("TITOLARE DI IMPRESA DI PULIZIE"),
         "preferredLabel"] <- 
    "Personal service workers"
  corpus[corpus$text%in%c("Programmatore",
                          "Coach di E-Sports, alleno dei giocatori di videogiochi attraverso dei campioni professionistici"),
         "preferredLabel"] <- 
    "Information and communications technology professionals"
  
  # merging corpus with data
  corpus$ID <- as.factor(corpus$ID)
  data <- plyr::join(data,corpus[,c("ID","preferredLabel")],by="ID",type="left")
  
  # replacing original job with recoded job categories
  data$job <- as.factor(data$preferredLabel)
  data$preferredLabel <- NULL # removing preferredLabel
  data$ID <- as.factor(data$ID)
  
  # summarizing info
  cat("Recoded job variable into",nlevels(data$job),"categories:\n")
  print(summary(data[!is.na(data$job),"job"]))
  return(data) }
```
</p>
</details>

``` {r message=FALSE, warning=FALSE}
prelqs <- job.recode(prelqs)
```

<br>

### 2.1.2. recoding workTime

Then, we recode the `workTime` variable, which was measured with an open-ended question as well.
``` {r}
# all lower case letters
workTimes <- wt.min <- wt.max <- tolower(prelqs$workTime)

# categorizing work start time
wt.min[grep("6:",wt.min)] <- # ............. # 6/7 = starting between 6:00 AM and 7:59 AM
  wt.min[grep("6.",wt.min)] <-  
  wt.min[grep("7:",wt.min)] <- 
  wt.min[grep("7.",wt.min)] <- "6/7"
wt.min[grep("8:",wt.min)] <- # ............... # 8 = starting between 8:00 AM and 8:59 AM
  wt.min[grep("8.",wt.min)] <-  "8"
wt.min[grep("9:",wt.min)] <- # ............ # 9/10 = starting between 9:00 AM and 9:59 AM
  wt.min[grep("9.",wt.min)] <- 
  wt.min[grep("10:",wt.min)] <- 
  wt.min[grep("10.",wt.min)] <- "9/10"
wt.min[grep("15-",wt.min)] <- # ............. # PM = starting in the afternoon
  wt.min[grep("15.",wt.min)] <- 
  wt.min[grep("14.",wt.min)] <- "PM"

# categorizing work end time
wt.max[grep("19:",wt.max)] <- # ............. # 19/21 = ending between 7:00 PM and 6:59 PM
  wt.max[grep("19.",wt.max)] <- 
  wt.max[grep("-19",wt.max)] <- 
  wt.max[grep("- 19",wt.max)] <- 
  wt.max[grep("- 20",wt.max)] <- 
  wt.max[grep("- 21",wt.max)] <- "19/21" 
wt.max[grep("17:",wt.max)] <- # ............. # 17/18 = ending between 5:00 PM and 6:59 PM
  wt.max[grep("17.",wt.max)] <- 
  wt.max[grep("-17",wt.max)] <- 
  wt.max[grep("- 17",wt.max)] <- 
  wt.max[grep("18:",wt.max)] <- 
  wt.max[grep("18.",wt.max)] <- 
  wt.max[grep("-18",wt.max)] <- 
  wt.max[grep("- 18",wt.max)] <- "17/18" 
wt.max[grep("15.",wt.max)] <-  #............. # 15/16 = ending between 3:00 PM and 4:59 PM
  wt.max[grep("15:",wt.max)] <- 
  wt.max[grep("-15",wt.max)] <- 
  wt.max[grep("16.",wt.max)] <- 
  wt.max[grep("16:",wt.max)] <- "15/16"
wt.max[grep("12:",wt.max)] <- # ............. # 12/14 = ending between noon and 2:59 PM
  wt.max[grep("12.",wt.max)] <- 
  wt.max[grep("13:",wt.max)] <- 
  wt.max[grep("13.",wt.max)] <- 
  wt.max[grep("_13",wt.max)] <-
  wt.max[grep("/13",wt.max)] <- 
  wt.max[grep("-13",wt.max)] <- 
  wt.max[grep("14:",wt.max)] <- 
  wt.max[grep("14.",wt.max)] <- 
  wt.max[grep("/14",wt.max)] <- 
  wt.max[grep("-2.",wt.max)] <- 
  wt.max[grep("- 02",wt.max)] <- "12/14" 

# joining start and end time
wt <- paste(wt.min,wt.max,sep="_")
wt[grep("PM_",wt)] <- "PM only" # correcting some cases and merging categories with few cases

# replacing workTime variable with recoded work times
prelqs$workTime <- as.factor(wt)
summary(prelqs$workTime)
```

<br>

## 2.2. Daily diaries

Here, we merge and recode the daily `diary` datasets.

### 2.2.1. Data cleaning

Here, we filter trial responses, we remove unuseful columns, and we rename and sort the relevant columns of the `diary` datasets.
``` {r}
# filtering trial/preview responses
diary_m <- diary_m[diary_m$DistributionChannel=="email",]
diary_m_sat <- diary_m_sat[diary_m_sat$DistributionChannel=="email",]
diary_a <- diary_a[diary_a$DistributionChannel=="email",]
diary_e <- diary_e[diary_e$DistributionChannel=="email",]

# removing unuseful variables
toRemove <- c("Duration..in.seconds.","Status","IPAddress","Progress","Finished","RecordedDate","ResponseId",
              "RecipientLastName","RecipientFirstName","LocationLatitude","LocationLongitude","DistributionChannel","UserLanguage")
diary_m[,toRemove] <- diary_a[,toRemove] <- diary_e[,toRemove] <- diary_m_mail[,toRemove] <-
  diary_a_mail[,toRemove] <- diary_m_sat[,toRemove] <- diary_m_sat_mail[,toRemove] <- diary_e_mail[,toRemove] <- NULL
```


<br>

### 2.2.2. Direct e-mails

Then, we merge each `diary` dataset with the corresponding data obtained by directly e-mailing participants (i.e., in case of technical/schedule problems).

``` {r}
# joining 'ordinary' datasets with datasets e-mailed datasets
diary_a_mail$RecipientEmail <- diary_a_mail$Q22_1 # afternoon
diary_a_mail$Q22_1 <- NULL
diary_a <- rbind(diary_a,diary_a_mail)
diary_e_mail$RecipientEmail <- diary_e_mail$e.mail_1 # evening
diary_e_mail$e.mail_1 <- NULL
diary_e <- rbind(diary_e,diary_e_mail)
diary_m_mail$RecipientEmail <- diary_m_mail$Q30_1 # morning
diary_m_mail$Q30_1 <- NULL
colnames(diary_m) <- gsub("\\.","",colnames(diary_m))
diary_m <- rbind(diary_m,diary_m_mail)
diary_m_sat_mail$RecipientEmail <- diary_m_sat_mail$Q11_1 # Saturday morning
diary_m_sat_mail$Q11_1 <- NULL
diary_m_sat_mail <- diary_m_sat_mail[!is.na(diary_m_sat_mail$RecipientEmail),] # removing trial entries
diary_m_sat <- rbind(diary_m_sat,diary_m_sat_mail)
diary_m_sat$dayOff_today <- diary_m_sat$AWL_1 <- diary_m_sat$AWL_2 <- diary_m_sat$AWL_3 <- NA # no AWL values on Saturday
diary_m <- rbind(diary_m,diary_m_sat)

# sorting and renaming columns
diary_m <- diary_m[,c(1:which(colnames(diary_m)=="ExternalReference"),which(colnames(diary_m)=="dayOff_today"),
                      which(colnames(diary_m)=="BP1_1"):which(colnames(diary_m)=="dayOff_yesterday"),
                      which(colnames(diary_m)=="AWL_1"):ncol(diary_m))]
colnames(diary_m)[1:9] <- colnames(diary_a)[1:9] <- colnames(diary_e)[1:9] <- c("start","end","ID","recruiter.ID","dayOff_today",
                                                                                "SBP1","DBP1","SBP2","DBP2")
colnames(diary_a)[5] <- colnames(diary_e)[5] <- "dayOff" # differentiating afternoon/evening from morning dayOff values 
colnames(diary_a) <- gsub("WHLSM1","WHLSM",gsub("WHLSM2","WHLSM",colnames(diary_a)))
```

<br>

### 2.2.3. Merging diary data

Here, we merge the four datasets into a single long-form `diary` dataset. The `diaryType` variable is created for discriminating among `M`orning, `A`fternoon, and `E`vening diaries. 
``` {r message=FALSE, warning=FALSE}
# adding diaryType column
diary_m$diaryType <- "M" # Morning diary
diary_a$diaryType <- "A" # Afternoon diary
diary_e$diaryType <- "E" # Evening diary

# Merging diary data (LONG dataset)
diary <- plyr::join(plyr::join(diary_m,diary_a,type="full"),diary_e,type="full")
cat("sanity check:",nrow(diary) == nrow(diary_m) + nrow(diary_a) + nrow(diary_e))
```
<br>

### 2.2.4. Data recoding

Then, we rename `diary` variables, we change the classes of the relevant variables, and we recode the levels of categorical (factor) variables consistently what we preregistered (see [preregistration here](https://osf.io/h9zvq)).
```{r}
# renaming columns
colnames(diary)[which(colnames(diary)=="lateWorkHours_1")] <- "lateWorkHours" # renaming column
colnames(diary)[which(colnames(diary)=="workHours_1")] <- "workHours" # renaming column
colnames(diary)[which(colnames(diary)=="waketime_16")] <- "wakeTime" # renaming column
colnames(diary) <- gsub("_","",colnames(diary))
colnames(diary)[grepl("SQ",colnames(diary))] <- paste("SQ",1:4,sep="")
colnames(diary)[which(colnames(diary)=="WL4")] <- "WL3"
colnames(diary)[grepl("WHLSM",colnames(diary))] <- paste("WHLSM",1:6,sep="")
colnames(diary)[which(colnames(diary)=="EE5")] <- "EE4"
colnames(diary)[grepl("R.det",colnames(diary))] <- paste("R.det",1:3,sep="")
colnames(diary)[grepl("R.rel",colnames(diary))] <- paste("R.rel",1:3,sep="")
colnames(diary)[grepl("R.mas",colnames(diary))] <- paste("R.mas",1:3,sep="")

# time (POSIXct variables)
diary$start <- as.POSIXct(diary$start)
diary$end <- as.POSIXct(diary$end)

# numeric
nums <- c(which(colnames(diary)=="SBP1"):which(colnames(diary)=="DBP2"),
          which(colnames(diary)=="AWL1"):which(colnames(diary)=="AWL3"),
          which(colnames(diary)=="OC1"):which(colnames(diary)=="SQ4"),
          which(colnames(diary)=="WL1"):which(colnames(diary)=="R.mas3"),
          which(colnames(diary)=="workHours"))
diary[,nums] <- lapply(diary[,nums],as.numeric)

# recording location
diary$where <- factor(gsub("1","workplace",
                           gsub("2","home",
                                gsub("4","other",diary$where))),levels=c("home","workplace","other"))
# dailyHassles
diary$dailyHassles <- as.factor(gsub("2","No",gsub("1","Yes",diary$dailyHassles)))

# diary type
diary$diaryType <- factor(diary$diaryType,levels=c("M","A","E"))

# dayOff_today & dayOff_yesterday as factor (recoding levels as FALSE = no, TRUE = yes)
diary$dayOff <- as.logical(as.numeric(diary$dayOff)-1)
diary$dayOfftoday <- as.logical(as.numeric(diary$dayOfftoday)-1)
diary$dayOffyesterday <- as.logical(as.numeric(diary$dayOffyesterday)-1)

# teleWork
diary$teleWork <- factor(gsub("1","teleWork",
                              gsub("2","office",
                                   gsub("3","both",
                                        gsub("4","dayOff",diary$teleWork)))),levels=c("office","teleWork","both","dayOff"))
# confounders
diary$confounders <- gsub("1","coffee",
                          gsub("2","smoke",
                               gsub("4","sport",
                                    gsub("5","meal",
                                         gsub("6","0",diary$confounders)))))
```

<br>

### 2.2.5. Recoding confounders

The `confounders` variable is processed by using the `checklist_sep` function in order to separate the different confounding factors.

<details><summary>`checklist_sep()`</summary>
<p>
``` {r}
checklist_sep <- function(data=NULL,varName=NULL,sep=",",nOptions=4,
                          labels=c("coffee","smoke","sport","meal"),return.data=FALSE){
  
  checklist <- data.frame(checklist_var=data[,varName])
  checklist$checklist_var <- as.character(checklist$checklist_var)
  for(i in 1:nOptions){ checklist$new.col <- NA
    if(!is.null(labels)){ colnames(checklist)[i+1] <- labels[i] } else { colnames(checklist)[i+1] <- paste("item",i,sep="") }}
  
  for(i in 1:nrow(checklist)){
    if(!is.na(checklist[i,1])){ checklist[i,2:ncol(checklist)] <- 0
      if(checklist[i,1]!="0"){ selected <- unlist(strsplit(checklist[i,1],sep))
        for(k in 1:length(selected)){ checklist[i,selected[k]] <- 1 }}}}
  checklist[,2:(nOptions+1)] <- lapply(checklist[,2:(nOptions+1)],as.factor)
  
  # adding new variables to data (excluding the category without confounders)
  data <- cbind(data[,1:which(colnames(data)==varName)],checklist[,2:(ncol(checklist)-1)],
                data[,(which(colnames(data)==varName)+1):ncol(data)])
  
  # recoding the original variable as logical (TRUE=any confounder category, FALSE=none)
  data$originalVar <- data[,varName]
  data[,varName] <- NA
  data[!is.na(data$originalVar),varName] <- TRUE
  data[!is.na(data$originalVar) & data$originalVar=="0",varName] <- FALSE
  data$originalVar <- NULL
  
  # printing summary
  print(summary(na.omit(checklist[,1:5])))
  
  # printing percentages
  out <- paste("\n\nPercentage of",varName,"over total number of occasions with non-missing values:\n")
  for(SC in labels){ colnames(checklist)[which(colnames(checklist)==SC)] <- "Var"
    out <- paste(out,"- ",SC,": ",
                 round(100*nrow(checklist[!is.na(checklist$checklist_var) & checklist$Var == "1",])
                       /nrow(checklist[!is.na(checklist$checklist_var),]),2),
                                  "%\n",sep="")
    colnames(checklist)[which(colnames(checklist)=="Var")] <- SC }
  cat(out)
  if(return.data==TRUE){return(data)}}
```
</p>
</details>

``` {r}
# number and percentages for each confounder
diary <- checklist_sep(data=diary,varName="confounders",return.data = TRUE)
```

``` {r}
# overall No. of cases with one or more confounders (604 TRUE, 2336 FALSE, 153 NA)
summary(diary$confounders)
```



<br>

### 2.2.6. wakeTime recoding

Then, we recode the `wakeTime` variable, which was measured with an
open-ended question.

``` {r}
# recoding wakeTime
wakeTimes <- diary$wakeTime
wakeTimes <- gsub("\\;",":",gsub("\\.","\\:",gsub("\\,","\\.",gsub("e",":",gsub(" ","",wakeTimes)))))
wakeTimes[!is.na(wakeTimes) & nchar(wakeTimes)==1] <- paste("0",wakeTimes[!is.na(wakeTimes) & nchar(wakeTimes)==1],":00",sep="")
wakeTimes[!is.na(wakeTimes) & nchar(wakeTimes)==2] <- paste(wakeTimes[!is.na(wakeTimes) & nchar(wakeTimes)==2],":00",sep="")
wakeTimes[!is.na(diary$wakeTime) & wakeTimes=="0615"] <- "05:15"
wakeTimes[!is.na(diary$wakeTime) & wakeTimes=="0800"] <- "08:00"
wakeTimes[!is.na(diary$wakeTime) & wakeTimes=="0830"] <- "08:30"
wakeTimes[!is.na(diary$wakeTime) & wakeTimes=="0700"] <- "07:00"
wakeTimes[!is.na(wakeTimes) & nchar(wakeTimes)==4] <- paste("0",wakeTimes[!is.na(wakeTimes) & nchar(wakeTimes)==4],sep="")
wakeTimes[!is.na(wakeTimes) & wakeTimes=="630"] <- "06:30"

# wakeTime as POSIXct & hhFromAwake
library(lubridate)
diary$wakeTime <- as.POSIXct(paste(substr(Sys.time(),1,10),wakeTimes),format="%Y-%m-%d %H:%M",tz="GMT")
diary$hhFromAwake <- as.numeric(difftime(as.POSIXct(paste(substr(Sys.time(),1,10),substr(diary$start,12,16)),
                                                    format="%Y-%m-%d %H:%M",tz="GMT"),diary$wakeTime,units="hours"))

# showing some example
head(diary[!is.na(diary$wakeTime),c("wakeTime","start","hhFromAwake")])
```

<br>

## 2.3. Data anonymization

Third, participants' `ID` (currently their e-mail addresses) are **anonymized** (i.e., replaced with alphanumeric code from `S001` to
`Snnn`).

Before anonymizing `ID` values, we filter cases only included in one but not in the other dataset.
``` {r}
# preparing prelqs data
prelqs$ID <- tolower(prelqs$ID) # all e-mails in lower capital
prelqs.IDs <- levels(as.factor(prelqs$ID)) # saving original IDs

# excluding pre-pilot diary data with the main author
diary <- diary[!(grepl("ini.job",diary$ID)|grepl("varo@st",diary$ID)|grepl("tti@stu",diary$ID)|grepl("tti@un",diary$ID)),]
diary$ID <- tolower(diary$ID) # all e-mails in lower capital

# correcting wrongly reported e-mail addresses in diary data
diary$ID <- gsub("tierno@studio.unibo.it","tierno@libero.it",diary$ID) 
diary$ID <- gsub("uzziell2","uzziello",diary$ID)
diary$ID <- gsub("uzziello@hotmail","uzziello@unibo",diary$ID)
diary$ID <- gsub("riarosa.m","rosa.m",gsub("o@auslromagna.it","o@gmail.com",diary$ID))
diary$ID <- gsub("o@auslcesena.it","o@gmail.com",diary$ID)
diary$ID <- gsub("gmsil","gmail",diary$ID)
diary$ID <- gsub("gmail.con","gmail.com",diary$ID)
diary$ID <- gsub("lli.robo@","lli.robi@",diary$ID)
diary$ID <- gsub("officia","officina",diary$ID)
diary$ID <- gsub("@liberi","@libero",gsub("@lidero","@libero",gsub("@llibro","@libero",diary$ID)))
diary$ID <- gsub("fcibien","f.cibien",gsub("klarco.com","klarco.it",diary$ID))
IDs <- levels(as.factor(diary$ID))

# sanity checks
cat(length(IDs),"participants in diary data,",length(prelqs.IDs),"in prelqs data\n",
    length(IDs[!(IDs%in%prelqs.IDs)]),"cases in diary but not in prelqs\n",
    length(prelqs.IDs[!(prelqs.IDs%in%IDs)]),"cases in prelqs but not in diary")
```

``` {r}
# removing cases only included prelqs but not in diary
memory <- prelqs
prelqs <- prelqs[prelqs$ID %in% IDs,]
prelqs.IDs <- levels(as.factor(prelqs$ID))
cat("Removed",nrow(memory)-nrow(prelqs),"participants only included in prelqs data")

# removing cases only included in diary but not in prelqs
memory <- diary
diary <- diary[diary$ID %in% prelqs.IDs,]
IDs <- levels(as.factor(diary$ID))
cat("Removed",nlevels(as.factor(memory$ID))-length(IDs),"participants,",nrow(memory)-nrow(diary),
    "observations only included in diary data")

# sanity checks
cat(length(IDs),"participants in diary data,",length(prelqs.IDs),"in prelqs data\n",
    length(IDs[!(IDs%in%prelqs.IDs)]),"cases in diary but not in prelqs\n",
    length(prelqs.IDs[!(prelqs.IDs%in%IDs)]),"cases in prelqs but not in diary")
```

<br>

We also correct a number of issues with `diary` time points due to participants answering to the wrong link.

``` {r}
# correcting surveys taken at wrong time points
diary[substr(diary$ID,15,27)=="rno@gmail.com" & 
        as.character(diary$start) %in% c("2021-10-26 21:26:43","2021-11-02 21:21:51"),
      "diaryType"] <- "E" # participant who responded twice to Afternoon diary in Evening time (keeping only BP values)
diary[substr(diary$ID,15,27)=="rno@gmail.com" & 
        as.character(diary$start) %in% c("2021-10-26 21:26:43","2021-11-02 21:21:51"),
      which(colnames(diary)=="WL1"):which(colnames(diary)=="WE3")] <- NA
diary[substr(diary$ID,8,13)=="isa.ma" & # participants that responded to a link open on the previous day
        !is.na(diary$WHLSM3) & as.character(diary$start)=="2022-01-25 10:45:00",
      "start"] <- as.character(as.POSIXct(diary[substr(diary$ID,8,13)=="isa.ma" & !is.na(diary$WHLSM3) & 
                                     as.character(diary$start)=="2022-01-25 10:45:00","end"]) -
  mean(as.numeric(difftime(diary[substr(diary$ID,8,13)=="isa.ma" & diary$start!="2022-01-25 10:45:00","end"],
                           diary[substr(diary$ID,8,13)=="isa.ma" & diary$start!="2022-01-25 10:45:00","start"],units="secs"))))
diary <- diary[!(substr(diary$ID,8,13)=="mo@mda" & diary$start%in%c("2022-01-25 16:39:22","2022-01-26 20:00:21")),] # double resp
diary[grepl("aia79",diary$ID) & as.character(diary$start)=="2022-03-10 07:25:32","diaryType"] <- 
  "M" # participant that responded to the Evening diary in Morning time (keeping only BP values and changing as Morning)
diary[grepl("aia79",diary$ID) & as.character(diary$start)=="2022-03-10 07:25:32",
      which(colnames(diary)=="EE1"):which(colnames(diary)=="workHours")] <- NA
diary <- # participant that responded twice to the Morning diary 
  diary[!(grepl("basso87",diary$ID) & as.character(diary$start)=="2022-03-07 19:53:47"),]

# correcting wrongly reported BP values
diary[substr(diary$ID,18,32)=="na.isasi.cnr.it" & as.character(diary$start)=="2021-11-25 19:17:27","SBP2"] <- 
  133 # reported as 88133
```

<br>

### 2.3.1. Preliminary questionnaire

Here, we anonymize `prelqs` `ID` values.
``` {r}
# replacing participants' e-mail with alphanumeric code (e.g., S001)
prelqs <- prelqs[order(prelqs$ID),] # sorting by ID
for(ID in prelqs.IDs){ id <- paste("S",which(prelqs.IDs==ID),sep="") 
  if(nchar(id)>2){ if(nchar(id)>3){ prelqs[prelqs$ID==ID,"ID"] <- id
  } else { prelqs[prelqs$ID==ID,"ID"] <- gsub("S","S0",id) }
    } else { prelqs[prelqs$ID==ID,"ID"] <- gsub("S","S00",id) }
  prelqs[prelqs$ID==ID,"ID"] <- ifelse(nchar(id)==3,id,paste("S0",which(prelqs.IDs==ID),sep=""))}
prelqs$ID <- as.factor(prelqs$ID)

# sanity check
head(levels(prelqs$ID))
cat(nlevels(prelqs$ID),"participants,",nrow(prelqs),"observations") # 147, 147
```

<br>

### 2.3.2. Daily diaries

Here, we anonymize `diary` `ID` values.
```{r}
# replacing participants' e-mail with alphanumeric code (e.g., S001)
diary <- diary[order(diary$ID,diary$start),] # sorting by ID and start time
for(ID in IDs){ id <- paste("S",which(IDs==ID),sep="") # replacing participants' e-mail with alphanumeric code (e.g., S001)
  if(nchar(id)>2){ if(nchar(id)>3){ diary[diary$ID==ID,"ID"] <- id
  } else { diary[diary$ID==ID,"ID"] <- gsub("S","S0",id) }
    } else { diary[diary$ID==ID,"ID"] <- gsub("S","S00",id) }
  diary[diary$ID==ID,"ID"] <- ifelse(nchar(id)==3,id,paste("S0",which(IDs==ID),sep=""))}
diary$ID <- as.factor(diary$ID)

# sanity check
head(levels(diary$ID))
```

``` {r}
cat(nlevels(diary$ID),"participants,",nrow(diary),"observations") # 147,3077
```

<br>

# 3. Data processing

As a final pre-processing step, we process `diary` data by including a `day` variable, by inspecting and recoding `BP` (blood pressure) and `BMI` (body mass index) data, by switching from the current long form to a **wide form** with one row per day, by merging the `diary` and the `prelqs` datasets, and by inspecting cases of incoherent/inconsistent responses in the considered variables.

## 3.1. Assessment day

First, we create the variable *day* indicating the day for participation (i.e., 1 = Monday, 2 = Tuesday, 3 = Wednesday, 4 = Thursday, 5 = Friday, 7 = Monday week II, 8 = Tuesday week II, 9 = Wednesday week II, 10 = Thursday week II, 11 = Friday week II). Note that this variable is recoded to be **referred to the previous day** in cases of **Evening diaries filled after 00:00**, and in **all Morning diaries**. We also remove 5 unexpected responses on day 6 and 12 (Saturday afternoon or Sunday).
``` {r}
# creating day
diary$day <- as.POSIXlt(diary$start)$wday

# adding 6 days to the second week
for(i in 2:nrow(diary)){
  if(diary[i,"ID"]==diary[i-1,"ID"] & diary[i,"day"] < diary[i-1,"day"]){ diary[i,"day"] <- diary[i,"day"] + 6 }}

# correcting evening days when start is after midnight
for(i in 1:nrow(diary)){
  if(diary[i,"diaryType"]=="E" & diary[i,"start"] < as.POSIXct(paste(substr(diary[i,"start"],1,10),"07:00:00"))){
    diary[i,"day"] <- diary[i,"day"] - 1 }}

# subtracting one day to all Morning diaries
diary[diary$diaryType=="M","day"] <- diary[diary$diaryType=="M","day"] - 1

# checking cases with unexpected values (6 or 12 = Morning on Mon, Evening/Afternoon on Sat)
table(diary$day)
```

<br>

``` {r}
# inspecting cases with unexpected values (6 or 12 = Morning on Mon, Evening/Afternoon on Sat)
cbind(wday=as.POSIXlt(diary[diary$day%in%c(6,12),"start"])$wday,
      diary[diary$day%in%c(6,12),c("ID","day","start","SBP1","WL1","EE1","SQ1","dayOff","dayOffyesterday")])
```

<br>

``` {r}
diary <- diary[!diary$day%in%c(6,12),] # removing 5 unexpected responses 

# sanity check
diary[,c("ID","start","diaryType","day")]
```

<br>

## 3.2. Blood pressure

Here, we visually inspect the distributions and temporal trends of **blood pressure** `BP` values against **normative values** and
separately by the **period of the day**. Then, we process the signal, and we compute the aggregate `BP` values.

First, we prepare the data for the inspection and processing, by creading a **long form** dataset with one row for each `BP` recording.

``` {r}
# preparing dataset
diary_long <- data.frame(ID=rep(diary$ID,4),measure=c(rep("SBP1",nrow(diary)),rep("SBP2",nrow(diary)),
                                                      rep("DBP1",nrow(diary)),rep("DBP2",nrow(diary))),
                         BP=c(diary$SBP1,diary$SBP2,diary$DBP1,diary$DBP2),diaryType=rep(diary$diaryType,4))
diary_long$gender=plyr::join(diary_long,prelqs[,c("ID","gender")],type="left",by="ID")$gender
diary_long$cv_d=paste(plyr::join(diary_long,prelqs[,c("ID","cv_dysf")],type="left",by="ID")$cv_dysf,
                      plyr::join(diary_long,prelqs[,c("ID","bp_drugs")],type="left",by="ID")$bp_drugs,sep="_")
```

<br>

### 3.2.1. BP distributions {#bp-distributions .tabset .tabset-fade .tabset-pills}

Here, we visualize `BP` distributions by plotting them against **normative values**.

#### vs. ESH/ESC Guidelines (2018)

First, we inspect the distribution of the observed `BP` measurements, accounting for the cut-off values recommended by the [ESH/ESC Guidelines (2018)](#ref). We can see that `BP` values are quite normally distributed (although with some cases showing bimodal distribution) and centered below the cut-offs for hypertension, with a **small proportion of measurements (\~ 9-11%) above the cut-offs**. Higher `SBP` and `DBP` is showed in participants reporting suffering from cardiovascular dysfunctions and/or taking BP medications. Slightly higher systolic `BP` is reported by men compared to women, and during afternoon compared to morning and evening. Overall, the data look realistic, although **diastolic BP looks bounded at 40 mmHg**.

``` {r fig.width=12,fig.height=8,warning=FALSE}
library(ggplot2); library(gridExtra)

# preparing dataset
diary_long <- data.frame(ID=rep(diary$ID,4),measure=c(rep("SBP1",nrow(diary)),rep("SBP2",nrow(diary)),
                                                      rep("DBP1",nrow(diary)),rep("DBP2",nrow(diary))),
                         BP=c(diary$SBP1,diary$SBP2,diary$DBP1,diary$DBP2),diaryType=rep(diary$diaryType,4))
diary_long$gender=plyr::join(diary_long,prelqs[,c("ID","gender")],type="left",by="ID")$gender
diary_long$cv_d=paste(plyr::join(diary_long,prelqs[,c("ID","cv_dysf")],type="left",by="ID")$cv_dysf,
                      plyr::join(diary_long,prelqs[,c("ID","bp_drugs")],type="left",by="ID")$bp_drugs,sep="_")

grid.arrange( # plotting sistolic and diastolic BP measurements
  ggplot(na.omit(diary_long),aes(x=measure,y=BP,group=measure)) + geom_point(color="gray",position=position_jitter()) + 
    geom_violin(alpha=0.4) + geom_boxplot(alpha=0.4,outlier.alpha=0) + ylab("Blood pressure (mmHg)") +
    geom_hline(yintercept = c(135,85),color="red",lty=2), # adding 2013 ESH/ESC Guidelines cut-offs for hypertension
  ggplot(na.omit(diary_long),aes(x=measure,y=BP,fill=gender)) + geom_point(color="gray",position=position_jitter()) + # by gender
    geom_violin(alpha=0.4) + geom_boxplot(alpha=0.4,outlier.alpha=0) + ylab("Blood pressure (mmHg)"),
  ggplot(na.omit(diary_long),aes(x=measure,y=BP,fill=cv_d)) + geom_point(color="gray",position=position_jitter()) + 
    geom_violin(alpha=0.4) + geom_boxplot(alpha=0.4,outlier.alpha=0) + ylab("Blood pressure (mmHg)"), # by cv_dysf & bp_drugs
  ggplot(na.omit(diary_long),aes(x=measure,y=BP,fill=diaryType)) + geom_point(color="gray",position=position_jitter()) + 
    geom_violin(alpha=0.4) + geom_boxplot(alpha=0.4,outlier.alpha=0) + ylab("Blood pressure (mmHg)"),nrow=2) # by time of day

# No. and % of SBP > 130 mmHG
ns <- nrow(diary_long[!is.na(diary_long$BP)&diary_long$measure%in%c("SBP1","SBP2")&diary_long$BP>135,])
nd <- nrow(diary_long[!is.na(diary_long$BP)&diary_long$measure%in%c("DBP1","DBP2")&diary_long$BP>85,])
cat(ns,"(",round(100*ns/nrow(diary_long[!is.na(diary_long$BP),]),1),"% ) SBP values above 130 mmHg\n",
    nd,"(",round(100*nd/nrow(diary_long[!is.na(diary_long$BP),]),1),"% ) DBP values above 85 mmHg\n")

# minumum diastolic BP
min(diary_long[diary_long$measure%in%c("DBP1","DBP2"),"BP"],na.rm=TRUE)
```

<br>

#### vs. Weisser et al (2000)

Here, we compare the observed BP values with **normative values** from [Weisser et al (2000)](#ref), based on self-measured and office BP from **503 healthy Swiss adults**. Red lines indicate the 35°, 58° and 76° percentile for office (solid) and self-measured (dotted) systolic BP, and the 50°, 67° and 78° percentile for diastolic BP, respectively. White dotted lines indicate the corresponding percentiles in the current sample. We can see that the observed values are **quite similar** to the normative ones, with greater similarities with home (\~3-5 mmHg) compared to office normative values (\~9 mmHg). It is confirmed that **DBP is bounded at 40 mmHg**, with a peak of cases corresponding to the lower limit.

``` {r fig.width=12,fig.height=5}
par(mfrow=c(2,2))
for(BP in c("SBP1","DBP1","SBP2","DBP2")){ hist(diary[,BP],main=BP,xlab="BP (mmHg)",breaks=50,col="black")
  abline(v=quantile(diary[,BP],na.rm=TRUE,probs=c(.35,.58,.76)),col="gray",lwd=2,lty=2) # observed quantiles
  if(substr(BP,1,1)=="S"){ abline(v=c(114.3,124.1,132.6),col="red",lwd=2,lty=2) # normative quantiles (systolic, home)
    abline(v=c(120,130,140),col="red",lwd=3) # normative quantiles (systolic, office)
  } else {  abline(v=c(75.1,79.9,85.8),col="red",lwd=2,lty=2) # normative quantiles (diastolic, home)
    abline(v=c(80,85,90),col="red",lwd=3) }} # normative quantiles (diastolic, office)ì
```

#### vs. Asayama et al (2019)

Then, we compare BP values obtained from Morning and Evening measures with those reported by [Asayama et al (2019)](#ref) for morning and
evening BP self-measured by 308 **hypertensive patients**. Red lines indicate normative mean ± 2 SD, whereas gray dotted lines indicate the
observed mean +/- 2 SD from the current sample. Again, data look 'realistic'.

``` {r fig.width=12,fig.height=5}
par(mfrow=c(2,2))
for(BP in c("SBP1","SBP2","DBP1","DBP2")){ hist(diary[,BP],main=BP,xlab="BP (mmHg)",breaks=50,col="black")
  abline(v=c(mean(diary[,BP],na.rm=TRUE),mean(diary[,BP],na.rm=TRUE)-2*sd(diary[,BP],na.rm=TRUE),
             mean(diary[,BP],na.rm=TRUE)+2*sd(diary[,BP],na.rm=TRUE)),col="gray",lwd=2,lty=2)
  if(substr(BP,1,1)=="S"){ abline(v=c(128.0,128.0+9.4*2,128.0-9.4*2),col="red",lwd=2) 
  } else {  abline(v=c(70.5,70.5+9.7*2,70.5-9.7*2),col="red",lwd=2) }} # normative mean +/- sd (diastolic)
```

<br>

### 3.2.2. BP by day time

Here, we visualize the distribution of **Systolic BP values** in Morning (M), Afternoon (A) and Evening (E) recordings, while also showing individual patterns of change. It is hard to determinate the prevalence of an increasing or decreasing pattern for both variables, although most cases seem to increase from Morning to Afternoon and decrease from Afternoon to Evening.

``` {r fig.width=12,fig.height=4,warning=FALSE}
# plotting SBP
diary$IDday <- as.factor(paste(diary$ID,diary$day,sep="_")) # adding ID x day factor
grid.arrange(ggplot(data=diary,aes(x=diaryType,y=SBP1)) + geom_line(aes(group=IDday,col=IDday)) + geom_violin(alpha=.4) +
               geom_boxplot(alpha=0.5,width=0.3,outlier.alpha=0) + theme(legend.position="none"),
             ggplot(data=diary,aes(x=diaryType,y=SBP2)) + geom_line(aes(group=IDday,col=IDday)) + geom_violin(alpha=.4) +
               geom_boxplot(fill=rgb(1,1,1,alpha=.4),width=0.3,outlier.alpha=0) + theme(legend.position="none"),nrow=1)

# plotting DBP
grid.arrange(ggplot(data=diary,aes(x=diaryType,y=DBP1)) + geom_line(aes(group=IDday,col=IDday)) + geom_violin(alpha=.4) +
               geom_boxplot(fill=rgb(1,1,1,alpha=.4),width=0.3) + ggtitle("Diastolic BP 1") + theme(legend.position="none"),
             ggplot(data=diary,aes(x=diaryType,y=DBP2)) + geom_line(aes(group=IDday,col=IDday)) + geom_violin(alpha=.4) +
               geom_boxplot(fill=rgb(1,1,1,alpha=.4),width=0.3) + ggtitle("Diastolic BP 2") + theme(legend.position="none"),nrow=1)
```

<br>

### 3.2.3. BP trajectories {#bp-trajectories .tabset .tabset-fade .tabset-pills}

Then, we use the `plotBP` function to visualize daily fluctuations in BP values. For each recording, the first and second entered value are
showed in light and dark blue, respectively. The red triangles show cases in which the second measurement was 10 mmHg higher or lower than
the second one, whereas the blue dotted line indicates the [ESH/ESC Guidelines (2018)](#ref) cut-off for hypertension. For both systolic and
diastolic BP time series, we **flag problematic cases** based on the visual inspection of steep BP increases/decreases compared to the others values from that participant.

<details><summary>`plotBP()`</summary>
<p>
``` {r}
plotBP <- function(data=NULL,BP="S",ncol=3,flagged=NA,what=NA,meanVal=NA){ require(ggplot2)
  
  # creating TIME by joining day and diaryType
  data$TIMElab <- paste(data$day,data$diaryType,sep="")
  data$TIME <- as.numeric(gsub("M",".0",gsub("A",".33",gsub("E",".66",data$TIMElab))))
  
  # setting graphic parameters
  cols <- colnames(data)[substr(colnames(data),1,3)==paste(BP,"BP",sep="")] # columns to be plotted
  maxScore <- max(data[,cols],na.rm=TRUE)
  data$disc <- abs(data[,cols[1]]-data[,cols[2]])
  data$disc10 <- FALSE
  data[!is.na(data$disc) & data$disc>=10,"disc10"] <- TRUE
  
  # plotting 
  p <- ggplot(data,aes_string(x="TIME",y=cols[1])) + 
    geom_line(colour="lightblue") + geom_point(colour="lightblue") + 
    scale_x_continuous(breaks=seq(1.33,12.66,length.out=11),labels=paste(1:11,"A",sep=""),position="top") +
    geom_line(aes_string(y=cols[2]),colour="blue") + 
    geom_point(aes_string(y=cols[2],shape="disc10"),size=2,color="blue") +
    geom_point(data=data[!is.na(data$disc) & data$disc>=10,],aes_string(y=cols[2],shape="disc10"),size=2,color="red") +
    facet_wrap("ID",strip.position = "right",ncol=ncol) +
    ggtitle(paste("Daily fluctuations in",ifelse(BP=="S","Systolic","Diastolic"),
                  "blood pressure (light blue = 1st recording, dark blue = 2nd recording, red triangles = discrep. > 10 mmHg)")) + 
    xlab("Day of week") + ylab(paste(BP,"BP (mmHg)",sep=""))+
    geom_hline(yintercept=ifelse(BP=="S",130,85),color="blue",lty=2)+
    theme(axis.text.x = element_text(size=9,angle = 45),legend.position="none")
  
  # marking flagged cases
  if(!is.na(flagged)){ data$flag <- data[,flagged]
    p <- p + geom_point(data=data[!is.na(data$flag) & data$flag==TRUE,],aes_string(y=cols[2]),size=4,shape=1) +
      geom_text(data=data[!is.na(data$flag) & data$flag==TRUE,],aes_string(y=paste(cols[2],"+10"),label=what)) }
  
  # showing average value
  if(!is.na(meanVal)){ data$meanVal <- data[,meanVal]
    p <- p + geom_line(data=data[!is.na(data$meanVal),],aes(y=meanVal),lwd=1.1,color="green") +
      geom_point(data=data[!is.na(data$meanVal),],aes(y=meanVal),color="green") }
  p}
```
</p>
</details>

<br>

#### SBP

``` {r fig.width=12,fig.height=80,warning=FALSE}
plotBP(diary,BP="S",ncol=4)
```

<br>

#### DBP

``` {r fig.width=12,fig.height=4,warning=FALSE}
plotBP(diary,BP="D",ncol=4)
```

<br>

#### SBP (flagged)

Here, we manually flag the cases showing extreme values in the **second measurement or both**, since we will remove the first measurement when it is more than 10 mmHg lower or higher than the first one (see below). Only these cases are plotted below.

``` {r fig.width=12,fig.height=8,warning=FALSE}
# flagged cases (only second = "2" or "both" measurement)
flagS <- data.frame(ID   = c("S006","S007","S011","S011","S030","S030","S033","S058","S061","S065","S077","S077"),
                   day   =     c(3,     2,     1,     8,    1,     2,    6,      8,     9,     10,   1,    1),
                   diaryType= c("A",   "E",   "A",   "A",  "A",   "A",  "M",    "A",   "E",   "M",   "A",  "E"),
                   whatS =     c("both","both","both","2",  "2",   "2",  "2",    "2",   "2",   "2",   "2",  "2"))
# further flagged cases
flagS <- rbind(flagS,
               data.frame(ID   = c("s080","s080","s080","S082","S100","S110","S110","S144"),
                   day   =     c(     1,    1,     2,     1,     1,     2,     3,      8),
                   diaryType= c(     "E",  "M",    "E",  "A",   "A",   "M",   "M",   "A"),
                   whatS =     c(   "both","both","both","both","both", "2",   "2",   "both")))

# adding information on flagged cases
flagS$SBPflag <- TRUE
diary <- plyr::join(diary,flagS,by=c("ID","day","diaryType"),type="left")
diary[is.na(diary$SBPflag) & !is.na(diary$SBP1),"SBPflag"] <- FALSE

# plotting
plotBP(diary[diary$ID%in%levels(as.factor(as.character(flagS$ID))),],BP="S",ncol=4,flagged="SBPflag",what="whatS")
```

<br>

#### DBP (flagged)

Here, we manually flag the cases showing extreme values in the **second measurement or both**, since we will remove the first measurement when it is more than 10 mmHg lower or higher than the first one (see below). Only these cases are plotted below.

``` {r fig.width=12,fig.height=8,warning=FALSE}
# flagged cases (only systolic = "S" or "both", but not diastolic - see below)
flagD <- data.frame(ID  = c(rep("S006",4),           "S007","S034","S057","S058","S058","S070","S070",rep("S080",3),"S082"),
                   day  = c(     1,  2,    2,  3,         5,     2,     9,    8,     8,       6,    8,       1,2,2,     1),
                   diaryType=c( "A","M",  "E","M",       "E",   "M",   "E",  "A",   "E",     "M",  "M",    "E","M","E","A"),
                   whatD =    c("2","inv","2","2",      "both", "2",  "2",   "2",   "2",     "2",  "2",  rep("both",3),"both"))

# further flagged cases
flagD <- rbind(flagD,
               data.frame(ID=c("S092","S093","S093","S093","S117","S117","S130","S144","S144","S145"),
                   day  =     c(  9,    2,      3,     7,    6,      9,     2,     8,     11,     1),
                   diaryType = c("E",  "A",    "A",   "E",  "M",    "A",   "E",   "A",    "E",   "A"),
                   whatD =    c(  "2", "inv",  "inv", "inv", "2",    "2",   "2",  "both","both", "2")))

# adding information on flagged cases
flagD$DBPflag <- TRUE
diary <- plyr::join(diary,flagD,by=c("ID","day","diaryType"),type="left")
diary[is.na(diary$DBPflag) & !is.na(diary$SBP1),"DBPflag"] <- FALSE

# plotting
plotBP(diary[diary$ID%in%levels(as.factor(as.character(flagD$ID))),],BP="D",ncol=4,flagged="DBPflag",what="whatD")
```

<br>

### 3.2.4. BP Aggregated values

Here, we aggregate the first and second measurement of systolic and diastolic `BP.` As [pre-registered here](https://osf.io/h9zvq), SBP and DBP aggregated values are computed as the **average between the first and the second** (immediately consecutive) `BP` recording. In all cases showing **discrepancies** (i.e., absolute differences) between the first and the second recording **equal to or higher than 10 mmHg, only the second recorded value** is considered (i.e., the first recording is excluded).

However, we also account for the **flagged cases** marked as "2" (when the second measurement should be removed), "both" (when both measurements have extreme values in the same direction), and "inv" (when the both have extreme values in opposite directions):

- in cases flagged as `"2"`, we only keep the first measurement

- in cases flagged as `"inv"`, we compute the average (as in any non-flagged case)

- in cases flagged as `"both"`, we interpolate the measurement as the maximum value (or the minimum, based on the direction of extreme values) recorded for that participant.

This is done independently for systolic and diastolic `BP`

<br>

#### 3.2.4.1. Aggregating values

First, we aggregate `SBP` values.
``` {r}
# SBP - printing info
cat("- Averaging",nrow(diary[!is.na(diary$SBPflag) & (diary$SBPflag==FALSE | diary$SBPflag=="inv") & 
                               abs(diary$SBP1-diary$SBP2)<10,]),"cases\n- Excluding",
    nrow(diary[!is.na(diary$SBPflag) & abs(diary$SBP1-diary$SBP2)>=10 & diary$SBPflag==FALSE,]),
    "first measurments\n- Excluding",nrow(diary[!is.na(diary$whatS) & diary$whatS=="2",]),
    "second mesurements\n- Interpolating",nrow(diary[!is.na(diary$whatS) & diary$whatS=="both",]),"measurements")


# (1) averaged cases (no flagged, or flagged = "inv", and discrepancies < 10mmHg)
diary[(!is.na(diary$SBPflag) & diary$SBPflag==FALSE & abs(diary$SBP1-diary$SBP2) < 10) |
        (!is.na(diary$whatS) & diary$whatS=="inv"),"SBP"] <- 
  apply(diary[(!is.na(diary$SBPflag) & diary$SBPflag==FALSE & abs(diary$SBP1-diary$SBP2) < 10) |
        (!is.na(diary$whatS) & diary$whatS=="inv"),c("SBP1","SBP2")],1,mean)
# (2) cases only based on the second measuremnt (discrepancies >= 10mmHg AND no flags)
diary[!is.na(diary$SBPflag) & diary$SBPflag==FALSE & abs(diary$SBP1-diary$SBP2) >= 10,"SBP"] <- 
  diary[!is.na(diary$SBPflag) & diary$SBPflag==FALSE & abs(diary$SBP1-diary$SBP2) >= 10,"SBP2"]
# (3) cases only based on the first measuremnt (flagged=TRUE and what = "2")
diary[!is.na(diary$SBPflag) & diary$SBPflag==TRUE & diary$whatS=="2",
      "SBP"] <- diary[!is.na(diary$SBPflag) & diary$SBPflag==TRUE & diary$whatS=="2","SBP1"]
# (4) cases interpolated as the maximum value
for(i in which(!is.na(diary$whatS) & diary$whatS=="both")){ 
  if(diary[i,"SBP2"]>mean(diary[diary$ID==diary[i,"ID"],"SBP"],na.rm=TRUE)){
    diary[i,"SBP"] <- max(diary[diary$ID==diary[i,"ID"],"SBP"],na.rm=TRUE)
  } else { diary[i,"SBP"] <- min(diary[diary$ID==diary[i,"ID"],"SBP"],na.rm=TRUE) }}

# sanity check
nrow(diary[(!is.na(diary$SBPflag) & is.na(diary$SBP))|(is.na(diary$SBPflag) & !is.na(diary$SBP)),]) == 0 
```

<br>

Then, we aggregate DBP values.

``` {r}
# DBP - printing info
cat("- Averaging",nrow(diary[((!is.na(diary$DBPflag) & diary$DBPflag==FALSE) | 
                               (!is.na(diary$whatD) & diary$whatD=="inv")) & 
                               abs(diary$DBP1-diary$DBP2)<10,]),"cases\n- Excluding",
    nrow(diary[!is.na(diary$DBPflag) & abs(diary$DBP1-diary$DBP2)>=10 & diary$DBPflag==FALSE,]),
    "first measurments\n- Excluding",nrow(diary[!is.na(diary$whatD) & diary$whatD=="2",]),
    "second mesurements\n- Interpolating",nrow(diary[!is.na(diary$whatD) & diary$whatD=="both",]),"measurements")
```

``` {r}
# (1) averaged cases (no flagged and discrepancies < 10mmHg, or flagged = "inv")
diary[((!is.na(diary$DBPflag) & diary$DBPflag==FALSE & abs(diary$DBP1-diary$DBP2) < 10) | 
         (!is.na(diary$whatD) & diary$whatD=="inv")),"DBP"] <- 
  apply(diary[((!is.na(diary$DBPflag) & diary$DBPflag==FALSE & abs(diary$DBP1-diary$DBP2) < 10) | 
         (!is.na(diary$whatD) & diary$whatD=="inv")),c("DBP1","DBP2")],1,mean)
# (2) cases only based on the second measurement (discrepancies >= 10mmHg AND no flags)
diary[!is.na(diary$DBPflag) & diary$DBPflag==FALSE & abs(diary$DBP1-diary$DBP2) >= 10,"DBP"] <- 
  diary[!is.na(diary$DBPflag) & diary$DBPflag==FALSE & abs(diary$DBP1-diary$DBP2) >= 10,"DBP2"]
# (3) cases only based on the first measurement (flagged=TRUE and what = "2")
diary[!is.na(diary$DBPflag) & !is.na(diary$whatD) & diary$DBPflag==TRUE & diary$whatD=="2",
      "DBP"] <- diary[!is.na(diary$DBPflag) & diary$DBPflag==TRUE & diary$whatD=="2","DBP1"]
# (4) cases interpolated as the maximum value
for(i in which(!is.na(diary$whatD) & diary$whatD=="both")){ 
  if(diary[i,"DBP2"]>mean(diary[diary$ID==diary[i,"ID"],"DBP"],na.rm=TRUE)){
    diary[i,"DBP"] <- max(diary[diary$ID==diary[i,"ID"],"DBP"],na.rm=TRUE)
  } else { diary[i,"DBP"] <- min(diary[diary$ID==diary[i,"ID"],"DBP"],na.rm=TRUE) }}

# sanity check
nrow(diary[(!is.na(diary$DBPflag) & is.na(diary$DBP))|(is.na(diary$DBPflag) & !is.na(diary$DBP)),]) == 0 
```

<br>

#### 3.2.4.2. Visual inspection {#visual-inspection .tabset .tabset-fade .tabset-pills}

Then, we replicate the plots generated above by showing the aggregated BP values in green.

##### SBP

``` {r fig.width=12,fig.height=80,warning=FALSE}
plotBP(diary,BP="S",ncol=4,flagged="SBPflag",what="whatS",meanVal="SBP")
```

<br>

##### DBP

``` {r}
plotBP(diary,BP="D",ncol=4,flagged="DBPflag",what="whatD",meanVal="DBP")
```

<br>

#### 3.2.4.3. Data integration

Finally, we replace the raw BP data with the processed (i.e., aggregate) values in the `diary` dataset and we remove the variables identifying flagged cases.

``` {r}
# replacing raw BP with aggregate BP
diary <- diary[,c(1:which(colnames(diary)=="dayOfftoday"),which(colnames(diary)%in%c("SBP","DBP")),
                  which(colnames(diary)=="dayOffyesterday"):(ncol(diary)-2))]

# creating new variable summarizing flagged cases
diary$flagBP <- FALSE
diary[(!is.na(diary$SBPflag) & diary$SBPflag==TRUE)|(!is.na(diary$DBPflag) & diary$DBPflag==TRUE),"flagBP"] <- TRUE
diary$SBPflag <- diary$whatS <- diary$DBPflag <- diary$whatD <- NULL # removing variables identifying flagged cases
```

<br>

## 3.3. BMI

Here, we inspect the distribution of `BMI` values, and we compare it with the cut-offs proposed by the [World Health Organization (2010)](#ref). We observe **no extreme BMI values** despite a few participants in the underweight (N = 3), and obesity - class II category (N = 8), and one participant in the obese - class III category. We have **no reasons to exclude** participants based on `BMI` values.

``` {r fig.width=12,fig.height=4,warning=FALSE}
hist(prelqs$BMI,xlab="BMI (kg/m^2)",breaks=30,xlim=c(15,40),ylim=c(0,14))
abline(v=c(18.5,25,30,35,40),col="red",lty=2)
text(x=c(16,21.5,27.5,32.5,37.5),y = 13,
     labels=c("Underweight","Normal weight","Pre-obesity","Obesity I","Obesity II"))
```

<br>

## 3.4. Daily wide dataset

Here, we use the `day` variable created above to transform the `diary` dataset from the current long form (i.e., one row per measurement
occasion within day and participant) to a daily wide form (i.e., one row per day within participant, with measurements collected on the same day being presented in the same line of the dataset).

First, we split the `diary` dataset back to the three datasets based on the measurement occasion (i.e., morning, afternoon, and evening).
``` {r}
# sorting columns
diary <- diary[,c("ID","day","start","end","diaryType","dayOff","dayOfftoday","dayOffyesterday",
                  "SBP","DBP","flagBP","where","confounders","coffee","smoke","sport","meal","dailyHassles",
                  colnames(diary)[which(colnames(diary)=="OC1"):which(colnames(diary)=="wakeTime")],"hhFromAwake",
                  paste("AWL",1:3,sep=""),
                  colnames(diary)[which(colnames(diary)=="WL1"):which(colnames(diary)=="workHours")],"IDday")]

# splitting dataset
diary_m <- diary[diary$diaryType=="M",c(1:which(colnames(diary)=="end"),
                                        which(colnames(diary)=="dayOfftoday"):which(colnames(diary)=="AWL3"),ncol(diary))]
diary_a <- diary[diary$diaryType=="A",c(1:which(colnames(diary)=="end"),which(colnames(diary)=="dayOff"),
                                        which(colnames(diary)=="SBP"):which(colnames(diary)=="meal"),
                                        which(colnames(diary)=="WL1"):which(colnames(diary)=="WE3"),ncol(diary))]
diary_e <- diary[diary$diaryType=="E",c(1:which(colnames(diary)=="end"),which(colnames(diary)=="dayOff"),
                                        which(colnames(diary)=="SBP"):which(colnames(diary)=="flagBP"),
                                        which(colnames(diary)=="confounders"):which(colnames(diary)=="dailyHassles"),
                                        which(colnames(diary)=="EE1"):which(colnames(diary)=="workHours"),ncol(diary))]

# renaming columns sharing the same name in the three datasets
cm <- colnames(diary_m)
ca <- colnames(diary_a)
ce <- colnames(diary_e)
colnames(diary_m)[3:ncol(diary_m)][which(colnames(diary_m)[3:ncol(diary_m)]%in%ca | colnames(diary_m)[3:ncol(diary_m)]%in%ce)] <-
  paste(colnames(diary_m)[3:ncol(diary_m)][which(colnames(diary_m)[3:ncol(diary_m)]%in%ca |
                                                   colnames(diary_m)[3:ncol(diary_m)]%in%ce)],"mor",sep="_")
colnames(diary_a)[3:ncol(diary_a)][which(colnames(diary_a)[3:ncol(diary_a)]%in%cm | colnames(diary_a)[3:ncol(diary_a)]%in%ce)] <-
  paste(colnames(diary_a)[3:ncol(diary_a)][which(colnames(diary_a)[3:ncol(diary_a)]%in%cm |
                                                   colnames(diary_a)[3:ncol(diary_a)]%in%ce)],"aft",sep="_")
colnames(diary_e)[3:ncol(diary_e)][which(colnames(diary_e)[3:ncol(diary_e)]%in%cm | colnames(diary_e)[3:ncol(diary_e)]%in%ca)] <-
  paste(colnames(diary_e)[3:ncol(diary_e)][which(colnames(diary_e)[3:ncol(diary_e)]%in%cm |
                                                   colnames(diary_e)[3:ncol(diary_e)]%in%ca)],"eve",sep="_")

# adding IDday column to join datasets based on ID and day
colnames(diary_m)[ncol(diary_m)] <- colnames(diary_a)[ncol(diary_a)] <- colnames(diary_e)[ncol(diary_e)] <- "IDday"
```

<br>

Second, we look for cases of **double responses** within each dataset by using the `ID` x `day` identifier created above. A total of six double responses are detected.
``` {r}
# diary_m: 1 double response -> taking 1st one
diary_m[diary_m$IDday%in%diary_m$IDday[duplicated(diary_m$IDday)],] 
diary_m <- diary_m[!(diary_m$ID=="S025" & as.character(diary_m$end_mor)=="2022-05-03 13:53:34"),]

# diary_a: 2 double responses -> taking 1st or 2d based on submission time
diary_a[diary_a$IDday%in%diary_a$IDday[duplicated(diary_a$IDday)],] 
diary_a <- diary_a[!((diary_a$ID=="S040" & as.character(diary_a$end_aft)=="2022-03-08 20:01:01") |
                       (diary_a$ID=="S060" & as.character(diary_a$end_aft)=="2022-01-25 18:58:10") |
                       (diary_a$ID=="S113" & as.character(diary_a$end_aft)=="2022-02-28 20:33:41") |
                       (diary_a$ID=="S113" & as.character(diary_a$end_aft)=="2022-03-01 21:41:58")),]

# diary_e: 3 double responses -> taking 2nd or 1st based on submission time
diary_e[diary_e$IDday%in%diary_e$IDday[duplicated(diary_e$IDday)],] 
```

<br>

``` {r}
diary_e <- diary_e[!((diary_e$ID=="S060" & as.character(diary_e$end_eve)=="2022-01-26 19:57:02") |
                       (diary_e$ID=="S108" & diary_e$dayOff_eve==TRUE)),]
# sanity check
nrow(diary_m[duplicated(diary_m$IDday),]) == 0 & nrow(diary_a[duplicated(diary_a$IDday),]) == 0 &
  nrow(diary_e[duplicated(diary_e$IDday),]) == 0
```

<br>

Now we can merge the tree dataset into a **daily wide form dataset**.

``` {r}
# joining datasets
diary_wide <- plyr::join(diary_a,plyr::join(diary_e,diary_m,by="IDday",type="full"),by="IDday",type="full")
diary_wide <- diary_wide[order(diary_wide$ID,diary_wide$day),] # sorting by ID and day

# sanity check
nrow(diary_wide[!is.na(diary_wide$start_aft),]) == nrow(diary_a) &
  nrow(diary_wide[!is.na(diary_wide$start_eve),]) == nrow(diary_e) & 
  nrow(diary_wide[!is.na(diary_wide$start_mor),]) == nrow(diary_m)


# checking for duplicates
nrow(diary_wide[duplicated(diary_wide$IDday),])
diary_wide$IDday <- NULL # removing IDday (no longer necessary)

# adding variables to filter missing data
diary_wide[,c("aft","eve","mor")] <- 0
diary_wide[!is.na(diary_wide$start_aft) & !is.na(diary_wide$dayOff_aft) & diary_wide$dayOff_aft==FALSE,"aft"] <- 1
diary_wide[!is.na(diary_wide$start_eve) & !is.na(diary_wide$dayOff_eve) & diary_wide$dayOff_eve==FALSE,"eve"] <- 1
diary_wide[!is.na(diary_wide$start_mor) & !is.na(diary_wide$dayOffyesterday) & diary_wide$dayOffyesterday==FALSE,"mor"] <- 1 
diary_wide[,c("aft","eve","mor")] <- lapply(diary_wide[,c("aft","eve","mor")],as.factor)
diary_wide <- diary_wide[,c(1:2,(ncol(diary_wide)-2):ncol(diary_wide),3:(ncol(diary_wide)-3))]

# sorting by ID and day
diary_wide <- diary_wide[order(diary_wide$ID,diary_wide$day),]
rownames(diary_wide) <- 1:nrow(diary_wide)

# showing dataset
diary_wide[,c(colnames(diary_wide)[1:6],"start_eve","start_mor")]
```


<br>

## 3.5. Joining prelqs

Here, we join the demographic and occupational information from the `prelqs` dataset with the time-varying data included in the `diary_wide` dataset. The resulting `diary_wide` dataset has multiple rows reporting the same demographic and occupational values (level-2) repeated over multiple rows associated with the same participant. Moreover, we also join the responses to the raw retrospective workaholism items.
``` {r}
# renaming retrospective workaholism items
colnames(prelqs)[grep("WHLSM",colnames(prelqs))] <- paste0("duwas",1:10)

# joining prelqs variables to diary_wide
diary_wide <- plyr::join(diary_wide,
                         prelqs[,c("ID",
                                   colnames(prelqs)[which(colnames(prelqs)=="gender"):which(colnames(prelqs)=="children")],
                                   "home_child","partner","home_partner",
                                   colnames(prelqs)[which(colnames(prelqs)=="smoker"):which(colnames(prelqs)=="workTime")],
                                   paste0("duwas",1:10))],
                         by="ID",type="left") # merging datasets by ID

# showing examples
head(diary_wide[,c(colnames(diary_wide)[1:6],"SBP_aft","age","sector")])
```

<br>

## 3.6. Response processing

Here, we look for cases of **incoherent/inconsistent responses** in several considered variables.

<br>

### 3.6.1. Days off

First, we check the cases of incoherent responses to the `dayOff` variable across the three daily diaries (e.g., participants reporting "*today, I did not work*" to the afternoon but not to the evening questionnaire). We can see a few incoherent cases (**2%**), mainly between afternoon and morning, in which participants reported a day-off at the end of the workday but an incoherent response on the following day. Since we have no criteria for establishing which was the 'true' answer, we assume that (un)voluntary mistakes were made for `dayOff=TRUE` responses, **treating them as missing responses**. Note that none of the cases with `dayOff_eve=FALSE` have `workHours=0`.
``` {r}
# 1) inconsistencies within Evening: dayOff_eve = FALSE but teleWork = "dayOff" (N = 4)
diary_wide[!is.na(diary_wide$teleWork) & diary_wide$teleWork=="dayOff",c("ID","day","teleWork","workHours")]
diary_wide[!is.na(diary_wide$teleWork) & diary_wide$teleWork=="dayOff","teleWork"] <- "office" # recoding as teleWork="office"

# 2) inconsistencies between Afternoon & Evening: dayOff_aft != dayOff_eve (N = 10)
diary_wide[!is.na(diary_wide$dayOff_aft) & !is.na(diary_wide$dayOff_eve) & diary_wide$dayOff_aft != diary_wide$dayOff_eve,
           c("ID","day","teleWork","workHours","aft","WL1","eve","EE1","dayOff_eve","dayOff_aft")]
diary_wide[!is.na(diary_wide$dayOff_aft) & !is.na(diary_wide$dayOff_eve) & 
             diary_wide$dayOff_aft==TRUE & diary_wide$dayOff_eve == FALSE,"dayOff_aft"] <- NA # replacing incorrect dayOff as NA
diary_wide[!is.na(diary_wide$dayOff_aft) & !is.na(diary_wide$dayOff_eve) & 
             diary_wide$dayOff_aft==FALSE & diary_wide$dayOff_eve == TRUE,"dayOff_eve"] <- NA

# 3) inconsistencies between Afternoon & Evening: dayOff_aft = FALSE but teleWork = "dayOff" (N = 0)
diary_wide[!is.na(diary_wide$dayOff_aft) & !is.na(diary_wide$teleWork) & 
             diary_wide$dayOff_aft==FALSE & diary_wide$teleWork=="dayOff",
           c("ID","day","teleWork","workHours","aft","WL1","eve","EE1","dayOff_eve","dayOff_aft")]

# 4) inconsistencies between Afternoon & Morning: dayOff_aft != dayOffyesterday (N = 4)
diary_wide[!is.na(diary_wide$dayOff_aft) & !is.na(diary_wide$dayOffyesterday) & diary_wide$dayOff_aft!=diary_wide$dayOffyesterday,
           c("ID","day","workHours","aft","WL1","mor","SQ1","dayOffyesterday","dayOff_aft")]
diary_wide[!is.na(diary_wide$dayOff_aft) & !is.na(diary_wide$dayOffyesterday) & 
             diary_wide$dayOff_aft==TRUE & diary_wide$dayOffyesterday == FALSE,"dayOff_aft"] <- NA # replacing with NA
diary_wide[!is.na(diary_wide$dayOff_aft) & !is.na(diary_wide$dayOffyesterday) & 
             diary_wide$dayOff_aft==FALSE & diary_wide$dayOffyesterday == TRUE,"dayOffyesterday"] <- NA

# 5) inconsistencies between Evening & Morning: dayOff_eve != dayOffyesterday (N = 2)
diary_wide[!is.na(diary_wide$dayOff_eve) & !is.na(diary_wide$dayOffyesterday) & diary_wide$dayOff_eve!=diary_wide$dayOffyesterday,
           c("ID","day","workHours","eve","EE1","mor","SQ1","dayOffyesterday","dayOff_eve")]
diary_wide[!is.na(diary_wide$dayOff_eve) & !is.na(diary_wide$dayOffyesterday) & diary_wide$dayOff_eve!=diary_wide$dayOffyesterday,
           "dayOff_eve"] <- NA # replacing unexpected value with NA

# 6) inconsistencies between Evening & Morning: teleWork = "dayOff" but dayOffyesterday=FALSE (N = 0)
diary_wide[!is.na(diary_wide$dayOffyesterday) & !is.na(diary_wide$teleWork) & 
             diary_wide$dayOffyesterday==FALSE & diary_wide$teleWork=="dayOff",
           c("ID","day","teleWork","workHours","eve","EE1","mor","SQ1","dayOff_eve","dayOffyesterday")]
```

<br>

Similarly, we inspect cases of participants reporting working days but answering "Today I did not work" to the `teleWork` item in the evening questionnaire, and vice versa. We can note that there are **no cases** of incoherent responses related to these variables.

``` {r}
# evening dayOff incoherent to evening telework (N = 0)
diary_wide[!is.na(diary_wide$dayOff_eve) & !is.na(diary_wide$telework) &
             ((diary_wide$dayOff_eve==FALSE & diary_wide$teleWork=="dayOff") |
                (diary_wide$dayOff_eve==TRUE & diary_wide$teleWork!="dayOff")),
           c("ID","dayOff_eve","teleWork","workHours")]

# afternoon dayOff incoherent to evening telework (N = 0)
diary_wide[!is.na(diary_wide$dayOff_aft) & !is.na(diary_wide$telework) &
             ((diary_wide$dayOff_aft==FALSE & diary_wide$teleWork=="dayOff") |
                (diary_wide$dayOff_aft==TRUE & diary_wide$teleWork!="dayOff")),
           c("ID","dayOff_aft","teleWork","workHours")]

# morning dayOffyesterday incoherent to evening telework (N = 0)
diary_wide[!is.na(diary_wide$dayOffyesterday) & !is.na(diary_wide$telework) &
             ((diary_wide$dayOffyesterday==FALSE & diary_wide$teleWork=="dayOff") |
                (diary_wide$dayOffyesterday==TRUE & diary_wide$teleWork!="dayOff")),
           c("ID","dayOffyesterday","teleWork","workHours")]
```

<br>

Finally, we inspect all cases with `workHours = 0`. We can see only a few of such cases (**2%**), all of which show `dayOff_eve=FALSE` (also because it was a necessary condition for responding to the `workHours` question) as well as `dayOff_aft=FALSE` and `dayOffyesterday=FALSE`. Due to this incoherence, we 'trust' the responses to `dayOff` items and we assume that these `workHours` responses were **careless responses**. Thus, we **interpolate** them by assigning the mean No.of `workHours` reported by the same participant in the other occasions. We also note one participant (`S137`) that reported `workHours=0` in all occasions. Thus, responses are interpolated based on the `weekHours` question from the `prelqs` data of the same participant, and we flag this participant as a potential `careless` respondent.

``` {r}
# inspecting cases of workHours = 0
cat(nrow(diary_wide[!is.na(diary_wide$workHours) & diary_wide$workHours==0,]),"cases of workHours = 0")

# inspecting responses to critical variables for such cases
diary_wide[!is.na(diary_wide$workHours) & diary_wide$workHours==0,
           c("ID","dayOff_aft","dayOff_eve","dayOffyesterday","workHours","lateWorkHours")]

# interpolating responses (i.e., assigning the mean No. of workHours for each participant)
IDs <- levels(as.factor(as.character(diary_wide[!is.na(diary_wide$workHours) & diary_wide$workHours==0,"ID"])))
for(ID in IDs[1:(length(IDs)-1)]){
  diary_wide[diary_wide$ID==ID & !is.na(diary_wide$workHours) & diary_wide$workHours==0,"workHours"] <-
    mean(diary_wide[diary_wide$ID==ID & !is.na(diary_wide$workHours) & diary_wide$workHours!=0,"workHours"])}

# interpolating responses of one participant that always reported workHours=0 based on prelqs weekHours
prelqs[prelqs$ID=="S137",c("ID","start","job","position","sector","weekHours","weekHours_remote","workTime")]
diary_wide[diary_wide$ID=="S137" & !is.na(diary_wide$dayOff_eve) & diary_wide$dayOff_eve==FALSE,"workHours"] <-
  prelqs[prelqs$ID=="S137","weekHours"]/5

# flagging S137 as a potential careless respondent
prelqs$careless <- FALSE
prelqs[prelqs$ID=="S137","careless"] <- TRUE
diary_wide <- plyr::join(diary_wide,prelqs[,c("ID","careless")],by="ID",type="left") # joining careless to diary_wide
```

<br>

### 3.6.2. Start time

Here, we inspect the distribution of `start` times looking for **filtering responses out of time** (e.g., morning diary filled in the evening).

``` {r fig.width=12,fig.height=3}
# plotting
par(mfrow=c(1,3))
hist(as.POSIXct(substr(diary_wide$start_aft,12,19),format="%H:%M:%S"),breaks="hours",main="Afternoon",xlab="")
hist(as.POSIXct(substr(diary_wide$start_eve,12,19),format="%H:%M:%S"),breaks="hours",main="Evening",xlab="")
hist(as.POSIXct(substr(diary_wide$start_mor,12,19),format="%H:%M:%S"),breaks="hours",main="Morning",xlab="")
```

<br>

#### 3.6.2.1. Filtering Afternoon

With flagged **Afternoon** responses given **after 22:00** (N = 11) :

-   we **keep** all responses that were submitted **up to one hour before** starting the Evening diary (N = 3)

-   we **only consider retrospective items** (i.e., `dayOff_aft`, `WL`, `WHLSM`, `WE`) and not momentary items (i.e., confounders and BP are removed) for responses submitted **less than one hour before** starting the Evening diary (N = 6)

-   we **recode momentary items** (i.e., BP, confounders) as they were responded **in the Evening** for all responses submitted later than 22:00 with **no subsequent Evening response** (i.e., BP values) but with nonmissing afternoon BP (N = 2)

``` {r}
# selecting flagged cases - afternoon (N = 11)
diary_wide$IDday <- paste(diary_wide$ID,diary_wide$day,sep="_") # re-creating IDday
flagA <- diary_wide[!is.na(diary_wide$start_aft) & (as.POSIXct(substr(diary_wide$start_aft,12,16),format="%H:%M") < # A after 22:00
                                                      as.POSIXct("15:00",format="%H:%M",tz="GMT") |
                                                      as.POSIXct(substr(diary_wide$start_aft,12,16),format="%H:%M") >
                                                      as.POSIXct("22:00",format="%H:%M",tz="GMT")),] 
flagA[,c("IDday","start_aft","end_aft","start_eve","workHours","lateWorkHours","workTime")] # showing flagged cases

# marking responses
flagA$flag <- NA
flagA[!is.na(flagA$start_eve) & difftime(flagA$start_eve,flagA$end_aft,units="hours")>1,"flag"] <- "ok"
flagA[!is.na(flagA$start_eve) & difftime(flagA$start_eve,flagA$end_aft,units="hours")<=1,"flag"] <- "momentary.OUT"
flagA[is.na(flagA$SBP_eve) & !is.na(flagA$SBP_aft),"flag"] <- "toEvening"
table(flagA$flag) # showing No. of cases

# filtering responses
diary_wide$flagA <- NA # flagging all flagA cases in the diary_wide dataset
diary_wide[diary_wide$IDday%in%levels(as.factor(flagA$IDday)),"flagA"] <- flagA[,"flag"]
aft_cols <- which(colnames(diary_wide)=="SBP_aft"):which(colnames(diary_wide)=="meal_aft") # afternoon momentary columns
eve_cols <- which(colnames(diary_wide)=="SBP_eve"):which(colnames(diary_wide)=="meal_eve") # evening momentary columns
diary_wide[!is.na(diary_wide$flagA) & diary_wide$flagA=="momentary.OUT",aft_cols] <- NA # removing momentary (N = 6)
diary_wide[!is.na(diary_wide$flagA) & diary_wide$flagA=="toEvening",eve_cols] <- # momentary values to Evening (N = 2)
  diary_wide[!is.na(diary_wide$flagA) & diary_wide$flagA=="toEvening",aft_cols[aft_cols!=which(colnames(diary_wide)=="where_aft")]]
diary_wide[!is.na(diary_wide$flagA) & diary_wide$flagA=="toEvening",
           aft_cols[aft_cols!=which(colnames(diary_wide)=="where_aft")]] <- NA # removing mom. from those cases

# sanity check
diary_wide[!is.na(diary_wide$flagA),c("flagA","end_aft","start_eve","SBP_aft","SBP_eve","WL1","EE1")]
```

<br>

#### 3.6.2.2. Filtering Evening

With flagged **Evening** responses (N = 16) given after 05:00:

-   we **keep** all responses that were submitted **between 21:00 and 5:00** and **up to one hour after** submitting the Afternoon diary (N = 8), by assuming that (a) in a few cases participant went to sleep around 21:30 (N = 7), and (b) responses that took 1+ hours (N = 1) were open early by mistake but responded at the right time (i.e., bedtime)

-   we **remove** all **momentary responses** (i.e., BP, confounders) that were **submitted less than one hour after** submitting the Afternoon diary (N = 2)

-   we **keep** all responses submitted **between 21:00 and 5:00** with **missing Afternoon** responses (N = 3)

-   we **remove** all responses submitted **after 05:00** when followed by the **morning surveys** with nonmissing BP measurements (N = 2)

-   we **recode momentary items** (i.e., BP, confounders) as they were responded **in the Morning**, while **removing** retrospective items for responses submitted later than 05:00 with **no subsequent Morning response** (N = 1)

``` {r}
# selecting flagged cases - evening (N = 16)
flagE <- diary_wide[!is.na(diary_wide$start_eve) & (as.POSIXct(substr(diary_wide$start_eve,12,16),format="%H:%M") < # E after 5:00
                                                      as.POSIXct("21:30",format="%H:%M",tz="GMT") &
                                                      as.POSIXct(substr(diary_wide$start_eve,12,16),format="%H:%M") >
                                                      as.POSIXct("05:00",format="%H:%M",tz="GMT")),] 
flagE$wakeTime <- substr(flagE$wakeTime,12,16)
flagE[,c("ID","end_aft","start_eve","end_eve","start_mor","wakeTime","workTime")] # showing flagged cases

# marking responses
flagE$flag <- NA
flagE[!is.na(flagE$start_aft) & difftime(flagE$end_eve,flagE$end_aft,units="hours")>=1 & # >21:00 & >1h from Aft (N = 8)
        as.POSIXct(substr(flagE$end_eve,12,16),format="%H:%M") > as.POSIXct("21:00",format="%H:%M",tz="GMT")  |
        as.POSIXct(substr(flagE$end_eve,12,16),format="%H:%M") < as.POSIXct("05:00",format="%H:%M",tz="GMT"),"flag"] <- "ok1"
flagE[!is.na(flagE$start_aft) & difftime(flagE$end_eve,flagE$end_aft,units="hours")<1,"flag"] <- "momentary.OUT" # >1h from Aft (2)
flagE[is.na(flagE$start_aft) & # >21:00 & without Aft (N = 3)
        as.POSIXct(substr(flagE$end_eve,12,16),format="%H:%M") > as.POSIXct("21:00",format="%H:%M",tz="GMT")  |
        as.POSIXct(substr(flagE$end_eve,12,16),format="%H:%M") < as.POSIXct("05:00",format="%H:%M",tz="GMT"),"flag"] <-"ok2"
flagE[!is.na(flagE$SBP_mor) & is.na(flagE$flag) & # >5:00 & nonmissing Morning BP (N = 2)
        as.POSIXct(substr(flagE$end_eve,12,16),format="%H:%M") > as.POSIXct("05:00",format="%H:%M",tz="GMT") &
        as.POSIXct(substr(flagE$end_eve,12,16),format="%H:%M") < as.POSIXct("21:00",format="%H:%M",tz="GMT"),"flag"] <-"OUT"
flagE[is.na(flagE$SBP_mor) & # >5:00 & missing Morning BP (N = 1)
        as.POSIXct(substr(flagE$end_eve,12,16),format="%H:%M") > as.POSIXct("05:00",format="%H:%M",tz="GMT") &
        as.POSIXct(substr(flagE$end_eve,12,16),format="%H:%M") < as.POSIXct("21:00",format="%H:%M",tz="GMT"),"flag"] <-"toMorning"
table(flagE$flag) # showing No. of cases

# filtering responses
diary_wide$flagE <- NA # flagging all flagM cases in the diary_wide dataset
eve_cols.full <- which(colnames(diary_wide)=="start_eve"):which(colnames(diary_wide)=="workHours") # evening full columns
mor_cols <- which(colnames(diary_wide)=="SBP_mor"):which(colnames(diary_wide)=="meal_mor") # morning momentary columns
diary_wide[diary_wide$IDday%in%levels(as.factor(flagE$IDday)),"flagE"] <- flagE[,"flag"]
diary_wide[!is.na(diary_wide$flagE) & diary_wide$flagE=="momentary.OUT",eve_cols] <- NA # removing momentary Evening (N = 2)
diary_wide[!is.na(diary_wide$flagE) & diary_wide$flagE=="OUT",eve_cols.full] <- NA # removing all Evening (N = 2)
diary_wide[!is.na(diary_wide$flagE) & diary_wide$flagE=="OUT","eve"] <- 0 
diary_wide[!is.na(diary_wide$flagE) & diary_wide$flagE=="toMorning",
           mor_cols[mor_cols!=which(colnames(diary_wide)=="where_mor")]] <- # momentary values to Morning (N = 1)
  diary_wide[!is.na(diary_wide$flagE) & diary_wide$flagE=="toMorning",eve_cols]
diary_wide[!is.na(diary_wide$flagE) & diary_wide$flagE=="toMorning",eve_cols] <- NA # removing mom. from those cases

# sanity check
diary_wide[!is.na(diary_wide$flagE),c("flagE","end_aft","end_eve","end_mor","SBP_eve","SBP_mor","EE1","OC1")]
```

<br>

#### 3.6.2.3. Filtering Morning

With flagged **Morning** responses (N = 23) given after 05:00:

-   we **keep** all responses started on **Saturdays before 15:00** (N = 6)

-   we **keep** all responses submitted on Tuesday-Friday **before 15:00** and **up to three hours before** submitting the Afternoon diary (N = 11)

-   we **remove** all responses submitted **after 15:00** (N = 4)

-   we **remove momentary items** from responses submitted **less than three hours before** submitting the Afternoon diary with nonmissing BP values (N = 1)

``` {r}
# selecting flagged cases - morning (N = 22)
flagM <- diary_wide[!is.na(diary_wide$start_mor) & (as.POSIXct(substr(diary_wide$start_mor,12,16),format="%H:%M") < # M after 12:00
                                                      as.POSIXct("23:59",format="%H:%M",tz="GMT") & 
                                                      as.POSIXct(substr(diary_wide$start_mor,12,16),format="%H:%M") >
                                                      as.POSIXct("12:00",format="%H:%M",tz="GMT")),]

# adding next day start_aft (start_aft.nd) when nonmissing
flagM$start_aft.nd <- as.POSIXct(NA)
flagM1 <- as.data.frame(matrix(nrow=0,ncol=ncol(flagM)))
for(i in 1:nrow(flagM)){ flagM1 <- rbind(flagM1,flagM[i,])
  if(nrow(diary_wide[diary_wide$ID==flagM[i,"ID"] & diary_wide$day==flagM[i,"day"]+1,])>0){ 
    flagM1[nrow(flagM1),"start_aft.nd"]<-diary_wide[diary_wide$ID==flagM[i,"ID"]&diary_wide$day==flagM[i,"day"]+1,"start_aft"]}}

# showing cases
flagM1[,c("ID","day","wakeTime","start_mor","end_mor","start_aft.nd")]
```

<br>

``` {r}
# marking responses
flagM1$flag <- NA
flagM1[flagM1$day%in%c(5,11) & # Saturdays with start <15:00 (N = 6)
       as.POSIXct(substr(flagM1$start_mor,12,16),format="%H:%M") < as.POSIXct("15:00",format="%H:%M",tz="GMT"),"flag"] <- "ok1"
flagM1[!flagM1$day%in%c(5,11) & difftime(flagM1$start_aft.nd,flagM1$end_mor,units="hours")>3 & # Tue-Fry <15:00 & >3h from Aft (11)
       as.POSIXct(substr(flagM1$start_mor,12,16),format="%H:%M") < as.POSIXct("15:00",format="%H:%M",tz="GMT"),"flag"] <- "ok2"
flagM1[as.POSIXct(substr(flagM1$start_mor,12,16),format="%H:%M") > # submitted >15:00 (N = 4)
         as.POSIXct("15:00",format="%H:%M",tz="GMT"),"flag"] <- "OUT"
flagM1[is.na(flagM1$flag),"flag"] <- "momentary.OUT" # submitted <3h before following Afternoon (N = 1)
table(flagM1$flag) # showing No. of cases
```
<br>

``` {r}
# filtering responses
diary_wide$flagM <- NA # flagging all flagM cases in the diary_wide dataset
mor_cols.full <- which(colnames(diary_wide)=="start_mor"):which(colnames(diary_wide)=="AWL3") # morning full columns
diary_wide[diary_wide$IDday%in%levels(as.factor(flagM1$IDday)),"flagM"] <- flagM1[,"flag"]
diary_wide[!is.na(diary_wide$flagM) & diary_wide$flagM=="momentary.OUT",mor_cols] <- NA # removing momentary Morning (N = 1)
diary_wide[!is.na(diary_wide$flagM) & diary_wide$flagM=="OUT",mor_cols.full] <- NA # removing all Morning (N = 4)
diary_wide[!is.na(diary_wide$flagM) & diary_wide$flagM=="OUT","mor"] <- 0

# sanity check
diary_wide[!is.na(diary_wide$flagM),c("flagM","start_mor","end_mor","SBP_mor","OC1")]
```

<br>

#### 3.6.2.4. Summary

In summary, we processed `diary_wide` responses by removing those obtained out of the scheduled times and by recoding time points based on the hour of the day. Specifically, we **removed 6 cases** (2 Morning, 4 Evening) with response times extremely out of bounds, we **removed momentary item responses from 9 cases** (6 Afternoon, 2 Evening, 1 Morning) with BP measurements too close to the preceding or the following ones, and we **recoded 3 cases** (2 Afternoon, 1 Evening) by assigning momentary item responses to the following diary. Here, we re-inspect the distribution of response times in the processed data.

``` {r fig.width=12,fig.height=3}
# creating new variable summarizing flagged cases
diary_wide$flagTime <- FALSE
diary_wide[(!is.na(diary_wide$flagA) & diary_wide$flagA!="ok") |
             (!is.na(diary_wide$flagE) & diary_wide$flagE!="ok1" & diary_wide$flagE!="ok2") |
             (!is.na(diary_wide$flagM) & diary_wide$flagM!="ok1" & diary_wide$flagM!="ok2"),"flagTime"] <- TRUE
diary_wide$flagA <- diary_wide$flagE <- diary_wide$flagM <- NULL # removing individual flag variables

# plotting
par(mfrow=c(1,3))
hist(as.POSIXct(substr(diary_wide[!is.na(diary_wide$SBP_aft),
                                  "start_aft"],12,19),format="%H:%M:%S"),breaks="hours",main="Aft",xlab="")
hist(as.POSIXct(substr(diary_wide[!is.na(diary_wide$SBP_eve),
                                  "start_eve"],12,19),format="%H:%M:%S"),breaks="hours",main="Eve",xlab="")
hist(as.POSIXct(substr(diary_wide[!is.na(diary_wide$SBP_mor),
                                  "start_mor"],12,19),format="%H:%M:%S"),breaks="hours",main="Mor",xlab="")
```

<br>

### 3.6.3. Work hours

Here, we inspect the distribution of `workHours` and `lateWorkHours` from the `diary_wide` dataset, and that of `weekHours` from the `prelqs` dataset, looking for cases of unrealistic numbers of working hours. We also compute the `workHours_total` variable as the sum of `workhours` and `lateWorkHours`.

However, we can see that the latter variable would be inaccurate as it might have value > 24h in some cases, implying that a number of participants did not consider `workhours` and `lateWorkhours` as mutually exclusive. Thus, we remove it from the dataset.

Moreover, whereas `workhours` seems quite realistic (ranging from 1h to 15h), **`lateWorkHours` looks inaccurately responded** by a number of participants that reported up to 10-20 hours of work after the 'ordinary' work schedule. Thus, we **dychotomize** `lateWorkHours` as `FALSE` (`lateWorkHours = 0`) or `TRUE` (`lateWorkHours > 0`)
``` {r fig.width=12,fig.height=3}
# computing workHours_total
diary_wide$workHours_total <- diary_wide$workHours + diary_wide$lateWorkHours

# plotting
par(mfrow=c(1,4)); hist(diary_wide$workHours,breaks=30); hist(diary_wide$lateWorkHours,breaks=30)
hist(diary_wide$workHours_total,breaks=30); hist(prelqs$weekHours,breaks=30)

# removing workHours_total
diary_wide$workHours_total <- NULL

# cases of lateWorkHours > 10
diary_wide[!is.na(diary_wide$lateWorkHours) & diary_wide$lateWorkHours>10,c("ID","day","workHours","lateWorkHours",
                                                                            "SQ1","wakeTime","workTime","weekHours",
                                                                            "weekHours_remote","job")]
```

<br>

``` {r}
# dychotomizing lateWorkHours
diary_wide$lateWorkHours1 <- NA
diary_wide[!is.na(diary_wide$lateWorkHours) & diary_wide$lateWorkHours==0,"lateWorkHours1"] <- FALSE
diary_wide[!is.na(diary_wide$lateWorkHours) & diary_wide$lateWorkHours>0,"lateWorkHours1"] <- TRUE
diary_wide$lateWorkHours <- diary_wide$lateWorkHours1
diary_wide$lateWorkHours1 <- NULL
summary(diary_wide$lateWorkHours) # summarizing cases  FALSE=702, TRUE=245, NA=197
```

<br>

### 3.6.4. BP confounders

Finally, we observe a number of cases (N = 2-7) with nonmissing response to the core `diary` items (e.g., workaholism, exhaustion, and sleep items) but missing response to the blood pressure confounders. Here, we set the confounders = `FALSE` for all of these cases.
``` {r}
# afternoon
cat(nrow(diary_wide[!is.na(diary_wide$WHLSM1) & is.na(diary_wide$confounders_aft),]),
    "cases with missing confounders_aft but nonmissing WHLSM1 item score")
diary_wide[!is.na(diary_wide$WHLSM1) & is.na(diary_wide$confounders_aft),"confounders_aft"] <- FALSE

# evening
cat(nrow(diary_wide[!is.na(diary_wide$EE1) & is.na(diary_wide$confounders_eve),]),
    "cases with missing confounders_eve but nonmissing EVE1 item score")
diary_wide[!is.na(diary_wide$EE1) & is.na(diary_wide$confounders_eve),"confounders_eve"] <- FALSE

# same thing for flagBP variables
diary_wide[!is.na(diary_wide$WHLSM_1) & is.na(diary$flagBP_aft),"flagBP_aft"] <- FALSE
diary_wide[!is.na(diary_wide$EE_1) & is.na(diary$flagBP_eve),"flagBP_eve"] <- FALSE
```

<br>

# 4. Data selection

Here, we select the observations, participants, and variables to be considered for the subsequent analyses. That is, we discard pilot participants and all related observations, and we remove the variables not considered in the present work.

## 4.1. Variable selection

Here, we subset the data by selecting and sorting the variables to be considered in the following analyses, whereas unused variables are not included.
``` {r}
# diary data                # case identification
diary_wide <- diary_wide[,c("ID","day",

                            # compliance
                            "aft","eve","mor",

                            # data quality
                            "flagTime","flagBP_aft","flagBP_eve","careless",

                            # afternoon diary
                            "start_aft","end_aft", # starting & ending date & time
                            "dayOff_aft", # took a day off (missing responses)
                            "SBP_aft","DBP_aft", # blood pressure
                            "where_aft","confounders_aft","coffee_aft","smoke_aft","sport_aft","meal_aft", # confounders
                            paste("WHLSM",1:6,sep=""), # state workaholism

                            # evening diary
                            "start_eve","end_eve", # starting & ending date & time
                            "dayOff_eve", # took a day off (missing responses)
                            "SBP_eve","DBP_eve", # blood pressure
                            "confounders_eve","coffee_eve","smoke_eve","sport_eve","meal_eve", # confounders
                            "teleWork","workHours","dailyHassles_eve", # further confounders/covariates
                            paste("EE",1:4,sep=""), # emotional exhaustion
                            paste0("R.det",1:3), # psychological detachment

                            # morning diary
                            "start_mor","end_mor", # starting & ending date & time
                            "dayOffyesterday", # took a day off yesterday or today (missing responses)
                            "lateWorkHours","wakeTime","hhFromAwake", # confounders/covariates
                            paste("SQ",1:4,sep=""), # sleep quality

                            # demographics from the preqls dataset
                            colnames(diary_wide)[which(colnames(diary_wide)=="gender"):which(colnames(diary_wide)=="weekHours")],
                            
                            # Retrospective workaholism items from the prelqs dataset
                            paste0("duwas",1:10))]
```

<br>

## 4.2. Case selection

Here, we subset the data by selecting the cases to be considered in the following analyses. That is, we **filter 12 pilot participants** that took part in the pilot trial of the study (i.e., before 2021-04-05).
``` {r}
# selecting pilot participants (12)
(pilots <- levels(as.factor(as.character(prelqs[prelqs$start<as.POSIXct("2021-04-05"),"ID"]))))

# filtering 12 pilot participants from diary data
memory <- diary
memory2 <- diary_wide
diary <- diary[!(diary$ID%in%pilots),]
diary_wide <- diary_wide[!(diary_wide$ID%in%pilots),]
diary$ID <- as.factor(as.character(diary$ID))
diary_wide$ID <- as.factor(as.character(diary_wide$ID))
cat("Removed",nlevels(memory$ID)-nlevels(diary$ID),"participants,",
    nrow(memory2)-nrow(diary_wide),"days,",nrow(memory)-nrow(diary),"observations")
cat("diary_wide: total No. of responses =",nrow(diary_wide),"from",nlevels(diary_wide$ID),"participants")
```

<br>

# 5. Data dictionary

Here, we provide a definition for each variable in the processed `diary_wide` datasets.

``` {r}
str(diary_wide)
```

<br>

**Identification**

-   `ID` = participant's identification code

-   `day` = day of participation (from 1 to 11)

<br>

**Compliance**

-   `aft` = day including the response to the Afternoon
    questionnaire (1) or not (0)

-   `eve` = day including the response to the Evening questionnaire (1)
    or not (0)

-   `mor` = day including the response to the Morning questionnaire (1)
    or not (0)

<br>

**Data quality**

-   `flagTime` = responses recoded due to wrong response timing (i.e.,
    responses given outside the scheduled intervals) (see section 3.6.2)

-   `flagBP_aft` - `flagBP_eve` = flagged cases that were reprocessed
    due to extreme BP values (see section 3.2)

-   `careless` = participant flagged as a careless respondent
    (`careless = TRUE`) due to inconsistent responses in the `DayOff`
    variables (see section 3.4.1) - the same variable is included in the
    `prelqs` dataset

<br>

**Afternoon questionnaire**

-   `start_aft` = starting time of the Afternoon questionnaire
    (yyyy-mm-dd hh:mm:ss)

-   `end_aft` = submission time of the Afternoon questionnaire
    (yyyy-mm-dd hh:mm:ss)

-   `dayOff_aft` = logical variable indicating whether the participant
    reported working on that day (FALSE) or not (TRUE)

-   `SBP_aft` - `DBP_aft` = systolic and diastolic aggregate blood
    pressure value (mmHg) measured in the Afternoon

-   `where_aft` = place where the Afternoon blood pressure recording was
    done ("home", "workplace", "other")

-   `confounders_aft` = logical variable indicating the presence (TRUE)
    or absence (FALSE) of any confounder before the Afternoon recording

-   `coffee_aft` - `meal_aft` = variables indicating the presence (1) or
    absence (0) of each confounder (i.e., cofee, smoke, sport, and meal)

-   `WHLSM1` - `WHLSM_6` = item scores at the diary version of the DUWAS
    (workaholism) (1-7)

<br>

**Evening questionnaire**

-   `start_eve` = starting time of the Evening questionnaire (yyyy-mm-dd
    hh:mm:ss)

-   `end_eve` = submission time of the Evening questionnaire (yyyy-mm-dd
    hh:mm:ss)

-   `dayOff_eve` = logical variable indicating whether the participant
    reported working on that day (FALSE) or not (TRUE)

-   `SBP_eve` - `DBP_eve` = systolic and diastolic aggregate blood
    pressure value (mmHg) measured in the Evening

-   `confounders_eve` = logical variable indicating the presence (TRUE)
    or absence (FALSE) of any confounder before the Evening recording

-   `coffee_eve` - `meal_eve` = variables indicating the presence (1) or
    absence (0) of each confounder (i.e., cofee, smoke, sport, and meal)

-   `teleWork` = factor indicating whether on that day the participant
    worked in the "office", did "teleWork", or "both"

-   `workHours` = number of working hours for that day (No.)

-   `dailyHassless_eve` = factor indicating whether the participant
    reported some daily hassles outside the working time on that day
    ("Yes") or not ("No")

-   `EE1` - `EE4` = item scores at the diary version of the Copenhagen
    Burnout Inventory (emotional exhaustion) (1-7)

-   `R.det1` - `R.det3` = item scores at the diary version of the Recovery Experiences Questionnaire (psychological detachment) (1-7)

<br>

**Morning questionnaire**

-   `start_mor` = starting time of the Morning questionnaire (yyyy-mm-dd
    hh:mm:ss)

-   `end_mor` = submission time of the Morning questionnaire (yyyy-mm-dd
    hh:mm:ss)

-   `dayOffyesterday` = logical variable indicating whether the
    participant reported working on the previous day day (FALSE) or not
    (TRUE)

-   `lateWorkHours` = logical variable indicating whether the
    participant reported working in the previous evening (TRUE) or not
    (FALSE)

-   `wakeTime` = self-reported waking time (yyyy-mm-dd hh:mm:ss)

-   `hhFromAwake` = number of hours between waketime and the response to
    the Morning questionnaire

-   `SQ` - `SQ4` = item scores at the diary version of the
    Mini Sleep Questionnaire (sleep disturbances) (1-7)

<br>

**Retrospective time-invariant variables** (measured with the preliminary questionnaire)

*Demographics*

-   `gender` = participant’s gender (“F” or “M”)

-   `age` = participant’s age (years)

-   `BMI` = participant’s body mass index (kg/m^2)

-   `edu` = participant’s education level (“middle”, “highschool”, “university+”)

-   `mStatus` = participant’s marital status (“single”, “partner”, “divorced”, “widowed”)

-   `home` = family situation (living “alone” or with “partner”, “children”, “parents”, “others”)

-   `children` = number of children (No.)

-   `home_child` = living with children (Yes/No)

-   `partner` = having a partner (Yes/No)

-   `home_partner` = living with partner (Yes/No)

*Confounders and inclusion criteria*

-   `smoker` = smoking status (“No”, “Yes”, “Quit_less”, “Quit_more”)

-   `bp_drugs` = participant reporting taking blood pressure medications (e.g., diuretics, beta-blokkants, anti-hypertension)

-   `horm_drugs` = participant reporting taking hormonal medications (e.g., birth control)

-   `psy_drugs` = participant reporting taking psychiatric drugs (e.g., antidepressants, anxiety)

-   `cv_dysf` = participant reporting suffering from a cardiovascular disease (e.g., hypertension, ischemia, strokes)

-   `sleep_dysf` = participant reporting suffering from a sleep-related disease (e.g., insomnia, parasomnia, sleep apnea)

*Occupational variables*

-   `job` = participant’s job recoded using the ISCO-08 classification of occupations (level 2) (Ganzeboom, 2010

-   `position` = participant’s job position (“Employee”, “Project”, “Manager”, “(Self-)Employer”)

-   `sector` = participant’s job sector (“Private” or “Public”)

-   `weekHours` = participant’s self-reported mean number of working hours per week (No.)

<br>

# 6. Data export

Here, we export the recoded and pre-processed `diary_wide` dataset (renamed as `diary`) to be used for further analyses. The datasets is exported in both CSV and RData format.
``` {r}
# exporting diary data
diary <- diary_wide # renamed as 'diary'
save(diary,file="DATI/diary_processed.RData") # RData
write.csv2(diary,file="DATI/diary_processed.csv", row.names=FALSE) # csv with ";"
```

<br>

# References {#ref}

- Ganzeboom, H. B. (2010). International standard classification of occupations ISCO-08 with ISEI-08 scores. Version of July, 27, 2010. Available from [this link](http://www.harryganzeboom.nl/Pdf/2010%20-%20Ganzeboom%20-%20A%20New%20International%20Socio-Economic%20Index%20ISEI%20of%20occupational%20status%20for%20the%20International%20Standard%20Classification%20of%20Occupation.pdf).

## R packages