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DAWBA_database_and_deletions.R
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#***********DAWBA = diagnostic information from participant screening
# to do:
# dawba deletions
# bdd information
# Loading patient info ----------------------------------------------------
if (exists("master_IRTA_latest")==FALSE) {
master_IRTA_latest <- read_excel(paste0(IRTA_tracker_location, "MASTER_IRTA_DATABASE.xlsx"))
date_variabes <- c("DOB", "Screening_Start_Date", "Referral_Date", "Consent_Date", "Clinical_Visit_Date", "Clinicals_date", "Overall_date")
for(i in seq_len(max_tasks)) { date_variabes <- c(date_variabes, paste0("Task", i, "_Date"))}
master_IRTA_latest[date_variabes] <- lapply(master_IRTA_latest[date_variabes], as.Date)
rm(i, date_variabes)
} else {
print("master participant tracker already imported")
}
if (exists("master_IRTA_screens_latest")==FALSE) {
master_IRTA_screens_latest <- read_excel(paste0(IRTA_tracker_location, "REFERRAL_AND_SCREENING_DATABASE.xlsx"))
date_variabes <- c("DOB", "Screening_Start_Date", "Referral_Date", "Consent_Date", "Clinical_Visit_Date", "Clinicals_date", "Overall_date")
for(i in seq_len(max_tasks)) { date_variabes <- c(date_variabes, paste0("Task", i, "_Date"))}
master_IRTA_screens_latest[date_variabes] <- lapply(master_IRTA_screens_latest[date_variabes], as.Date)
rm(i, date_variabes)
} else {
print("master current screens tracker already imported")
}
if (exists("master_IRTA_old_screens_latest")==FALSE) {
master_IRTA_old_screens_latest <- read_excel(paste0(IRTA_tracker_location, "OLD_REFERRALS_DATABASE.xlsx"))
date_variabes <- c("DOB", "Screening_Start_Date", "Referral_Date", "Consent_Date", "Clinical_Visit_Date", "Clinicals_date", "Overall_date")
for(i in seq_len(max_tasks)) { date_variabes <- c(date_variabes, paste0("Task", i, "_Date"))}
master_IRTA_old_screens_latest[date_variabes] <- lapply(master_IRTA_old_screens_latest[date_variabes], as.Date)
rm(i, date_variabes)
} else {
print("master old referrals tracker already imported")
}
# Isolating participant info I need & merging -----------------------------
master_IRTA_identifiers <- master_IRTA_latest %>%
select(FIRST_NAME, LAST_NAME, Initials, DAWBA_ID, PLUSID, SDAN, IRTA_tracker, Participant_Type2,
Clinical_Visit_Date, Screening_Start_Date, Referral_Date, Overall_date,
Eligible, Eligibility_notes, Scheduling_status, Scheduling_status_notes,
Parent_e_consented, Child_e_assented, Parent_DAWBA_completed, Child_DAWBA_completed, DAWBA_completed) %>%
group_by(Initials) %>% fill(., DAWBA_ID, PLUSID, .direction = c("down")) %>% fill(., DAWBA_ID, PLUSID, .direction = c("up")) %>%
arrange(Initials, Overall_date) %>% filter(1:n() == 1) %>% ungroup() %>%
mutate(Source = "Current participants") %>% filter(!is.na(DAWBA_ID) | !is.na(PLUSID))
master_current_screen_identifiers <- master_IRTA_screens_latest %>%
select(FIRST_NAME, LAST_NAME, Initials, DAWBA_ID, PLUSID, SDAN, IRTA_tracker, Participant_Type2,
Clinical_Visit_Date, Screening_Start_Date, Referral_Date, Overall_date,
Eligible, Eligibility_notes, Scheduling_status, Scheduling_status_notes,
Parent_e_consented, Child_e_assented, Parent_DAWBA_completed, Child_DAWBA_completed, DAWBA_completed) %>%
mutate(Source = "Current screens") %>% filter(!is.na(DAWBA_ID) | !is.na(PLUSID))
master_old_screen_identifiers <- master_IRTA_old_screens_latest %>%
select(FIRST_NAME, LAST_NAME, Initials, DAWBA_ID, PLUSID, SDAN, IRTA_tracker, Participant_Type2,
Clinical_Visit_Date, Screening_Start_Date, Referral_Date, Overall_date,
Eligible, Eligibility_notes, Scheduling_status, Scheduling_status_notes,
Parent_e_consented, Child_e_assented, Parent_DAWBA_completed, Child_DAWBA_completed, DAWBA_completed) %>%
mutate(Source = "OLD screens") %>% filter(!is.na(DAWBA_ID) | !is.na(PLUSID))
participant_identifiers_combined <- merge.default(master_IRTA_identifiers, master_current_screen_identifiers, all=TRUE) %>%
merge.default(., master_old_screen_identifiers, all=TRUE)
participant_identifiers_combined <- participant_identifiers_combined %>% group_by(Initials) %>%
fill(., names(participant_identifiers_combined), .direction = c("down")) %>%
fill(., names(participant_identifiers_combined), .direction = c("up")) %>%
arrange(Initials, Source) %>% filter(1:n() == 1) %>% ungroup()
# finish the split below
split1 <- colsplit(participant_identifiers_combined$DAWBA_ID, "/", names = c("DAWBA1", "DAWBA2"))
participant_identifiers_combined <- cbind(participant_identifiers_combined, split1)
participant_identifiers_combined <- melt(data = participant_identifiers_combined, id.vars =
c("FIRST_NAME", "LAST_NAME", "Initials", "PLUSID", "SDAN", "IRTA_tracker", "Participant_Type2", "Clinical_Visit_Date", "Screening_Start_Date",
"Referral_Date", "Overall_date", "Parent_e_consented", "Child_e_assented", "Parent_DAWBA_completed", "Child_DAWBA_completed", "DAWBA_completed",
"Eligible", "Eligibility_notes", "Scheduling_status", "Scheduling_status_notes", "Source"), measure.vars = c("DAWBA1", "DAWBA2")) %>%
rename(DAWBA_ID = value) %>% mutate(DAWBA_ID = as.character(DAWBA_ID)) %>% filter(variable=="DAWBA1" | !is.na(DAWBA_ID)) %>% select(-variable)
# DAWBA import ------------------------------------------------------------
# new DAWBA import:
DAWBA_Data_Download_raw <- read.delim(paste0(dawba_pull, latest_dawba_pull, ".csv"), sep="\t", quote="", encoding="UTF-8", row.names = NULL, header = TRUE, stringsAsFactors = FALSE)
DAWBA_Data_Download_raw[DAWBA_Data_Download_raw==-2] <- NA
DAWBA_Data_Download_raw %>% write_xlsx(paste0(dawba_pull, "old/DAWBA_", todays_date_formatted, "_raw.xlsx"))
# dawba_col_names <- DAWBA_Data_Download_raw %>% colnames() %>% as.data.frame()
# dawba_col_names %>% write_xlsx(paste0(dawba_pull, "DAWBA_colnames_", todays_date_formatted, ".xlsx"))
# importing existing DAWBA archive:
DAWBA_Archive <- read_excel(paste0(database_location, "other_data_never_delete/dawba_archive_raw.xlsx"))
# merging old & new, clean up & then save new DAWBA archive
dawba_combined <- merge.default(DAWBA_Archive, DAWBA_Data_Download_raw, all=TRUE) %>%
filter(sid !="234110") %>% filter(sid !="234111") %>% filter(sid !="234112") %>% filter(sid !="234113") %>% filter(sid !="None")
dawba_combined <- dawba_combined %>%
group_by(sid) %>%
fill(., names(dawba_combined), .direction = "down") %>%
fill(., names(dawba_combined), .direction = "up") %>%
ungroup() %>%
distinct(., .keep_all = TRUE)
dawba_combined %>% write_xlsx(paste0(dawba_pull, "old/dawba_archive_raw_", todays_date_formatted, ".xlsx"))
dawba_combined %>% write_xlsx(paste0(database_location, "other_data_never_delete/dawba_archive_raw.xlsx"))
# reducing down to the DAWBA variables we're interested in right now
dawba_columns <- read_excel(paste0(database_location, "other_data_never_delete/dawba_column_names_and_descriptions.xlsx"))
setnames(dawba_combined, old=c(dawba_columns$old_name), new=c(dawba_columns$new_name), skip_absent=TRUE)
DAWBA_Data_Download <- dawba_combined %>% select(dawba_columns$new_name) %>% arrange(DAWBA_ID)
# Clean up -------------------------------------------
count_na <- function(x) sum(is.na(x))
DAWBA_Data_Download$DAWBA_SEX[DAWBA_Data_Download$DAWBA_SEX==1] <- 'MALE'
DAWBA_Data_Download$DAWBA_SEX[DAWBA_Data_Download$DAWBA_SEX==2] <- 'FEMALE'
dawba_date_variables <- c("p_dawba_sdq_date", "s_dawba_sdq_date")
DAWBA_Data_Download[dawba_date_variables] <- lapply(DAWBA_Data_Download[dawba_date_variables], as.Date, "%d.%m.%y")
dawba_w_names <- merge.default(participant_identifiers_combined, DAWBA_Data_Download, all=TRUE) %>% filter(!is.na(Initials) | !is.na(dawba_logins))
dawba_w_names$Eligible <- recode(dawba_w_names$Eligible, "0"="Include",
"1"="Include: can't scan (braces, etc.)","2"="On hold: contact again after specified amount of time","3"="On hold: low priority",
"4"="Excluded: cannot be reached or scheduled, all contact options exhausted ",
"5"="Excluded: does not meet criteria",
"6"="Excluded: meets exclusionary criteria (substance use, psychosis, etc.)",
"7"="Did not or withdrew assent/consent", "8"="Ruled as ineligible for treatment during baseline assessment (didn't meet inclusionary or met exclusionary criteria)",
"9"="Patient (or parent) withdrew from treatment", "10"="Excluded after commencing treatment: some treatment received before participant was later excluded (e.g. bad scanner, now meets exclusionary criteria, etc.)",
"11"="Completed treatment", .missing = NULL)
# Adding BDD predictions --------------------------------------------------
#####
# Parent
# criterion a = a lot of worry beyond normal (pz1) plus a lot of worry about a specific body part (pz2a-pz2i)
pca <- dawba_w_names %>% select(DAWBA_ID, PLUSID, Initials, p_dawba_bdd_1_concerns_appearance, matches("p_dawba_bdd_2"), -p_dawba_bdd_2_text)
pca[,4:ncol(pca)] <- lapply(pca[,4:ncol(pca)], as.numeric)
pca$no_columns <- pca %>% select(p_dawba_bdd_1_concerns_appearance, matches('p_dawba_bdd_2')) %>% ncol() %>% as.numeric()
pca$NA_count <- pca %>% select(p_dawba_bdd_1_concerns_appearance, matches('p_dawba_bdd_2')) %>% apply(., 1, count_na)
pca$diff <- c(pca$no_columns - pca$NA_count)
pca <- pca %>% filter(diff>0) %>% select(-no_columns, -NA_count, -diff)
pca$p_dawba_bdd_2_sum <- pca %>% select(matches("p_dawba_bdd_2")) %>% rowSums(na.rm=TRUE)
pca2 <- pca %>% filter(p_dawba_bdd_1_concerns_appearance=="2" |
(p_dawba_bdd_1_concerns_appearance=="1" &
(p_dawba_bdd_2a_skin_condition=="2" | p_dawba_bdd_2b_skin_colour=="2" | p_dawba_bdd_2c_hair_colour_or_condition=="2" |
p_dawba_bdd_2d_muscle_bulk=="2" | p_dawba_bdd_2e_body_shape_or_size=="2" |
p_dawba_bdd_2f_facial_features=="2" | p_dawba_bdd_2g_other_body_part=="2" |
p_dawba_bdd_2h_asymmetry=="2" | p_dawba_bdd_2i_other_aspect_of_appearance=="2"))) %>%
mutate(p_dawba_bdd_criterion_a = 1) %>% select(DAWBA_ID, p_dawba_bdd_criterion_a)
pca <- left_join(pca, pca2, all=TRUE) %>% select(DAWBA_ID, PLUSID, Initials, p_dawba_bdd_2_sum, p_dawba_bdd_criterion_a)
# criterion b = repetitive behaviours
pcb <- dawba_w_names %>% select(DAWBA_ID, PLUSID, Initials, matches("p_dawba_bdd_4"))
pcb[,4:ncol(pcb)] <- lapply(pcb[,4:ncol(pcb)], FUN = function(x) recode(x, `0`=0, `1`=0, `2`=1, .missing = NULL))
pcb$no_columns <- pcb %>% select(matches('p_dawba_bdd_4')) %>% ncol() %>% as.numeric()
pcb$NA_count <- pcb %>% select(matches('p_dawba_bdd_4')) %>% apply(., 1, count_na)
pcb$diff <- c(pcb$no_columns - pcb$NA_count)
pcb <- pcb %>% filter(diff>0) %>% select(-no_columns, -NA_count, -diff)
pcb$p_dawba_bdd_4_sum <- pcb %>% select(matches("p_dawba_bdd_4")) %>% rowSums(na.rm=TRUE)
pcb <- pcb %>% mutate(p_dawba_bdd_criterion_b = ifelse(p_dawba_bdd_4_sum>2, 1, NA)) %>%
select(DAWBA_ID, PLUSID, Initials, p_dawba_bdd_4_sum, p_dawba_bdd_criterion_b)
# criterion c = clinically significant distress
pcc <- dawba_w_names %>%
select(DAWBA_ID, PLUSID, Initials, p_dawba_bdd_5a_time_spent_worrying_appearance,
p_dawba_bdd_5b_time_spent_hiding_improving_appearance,
p_dawba_bdd_8_distress, p_dawba_bdd_9a_impact_on_family_life,
p_dawba_bdd_9b_impact_on_friendships, p_dawba_bdd_9c_impact_on_learning,
p_dawba_bdd_9d_impact_on_leisure)
pcc[,4:5] <- lapply(pcc[,4:5], FUN = function(x) recode(x, `0`=0, `1`=0, `2`=0, `3`=3, `4`=3, .missing = NULL))
pcc <- pcc %>% filter(p_dawba_bdd_5a_time_spent_worrying_appearance=="3" | p_dawba_bdd_5b_time_spent_hiding_improving_appearance=="3" |
p_dawba_bdd_8_distress=="3" | p_dawba_bdd_9a_impact_on_family_life=="3" |
p_dawba_bdd_9b_impact_on_friendships=="3" | p_dawba_bdd_9b_impact_on_friendships=="3" |
p_dawba_bdd_9c_impact_on_learning=="3" | p_dawba_bdd_9d_impact_on_leisure=="3") %>%
mutate(p_dawba_bdd_criterion_c = 1) %>%
select(DAWBA_ID, PLUSID, Initials, p_dawba_bdd_criterion_c)
# recombining
p_bdd_combined <- merge.default(pca, pcb, all=TRUE) %>% merge.default(., pcc, all=TRUE)
p_bdd_combined$p_dawba_bdd_criterion_a[is.na(p_bdd_combined$p_dawba_bdd_criterion_a)] <- 0
p_bdd_combined$p_dawba_bdd_criterion_b[is.na(p_bdd_combined$p_dawba_bdd_criterion_b)] <- 0
p_bdd_combined$p_dawba_bdd_criterion_c[is.na(p_bdd_combined$p_dawba_bdd_criterion_c)] <- 0
# determining whether a diagnosis is met
p_bdd_combined <- p_bdd_combined %>% mutate(p_dawba_bdd_diag = (as.numeric(p_dawba_bdd_criterion_a) + as.numeric(p_dawba_bdd_criterion_b) + as.numeric(p_dawba_bdd_criterion_c)))
#####
# Child
sca <- dawba_w_names %>% select(DAWBA_ID, PLUSID, Initials, s_dawba_bdd_1_concerns_appearance, matches("s_dawba_bdd_2"), -s_dawba_bdd_2_text)
sca[,4:ncol(sca)] <- lapply(sca[,4:ncol(sca)], as.numeric)
sca$no_columns <- sca %>% select(s_dawba_bdd_1_concerns_appearance, matches('s_dawba_bdd_2')) %>% ncol() %>% as.numeric()
sca$NA_count <- sca %>% select(s_dawba_bdd_1_concerns_appearance, matches('s_dawba_bdd_2')) %>% apply(., 1, count_na)
sca$diff <- c(sca$no_columns - sca$NA_count)
sca <- sca %>% filter(diff>0) %>% select(-no_columns, -NA_count, -diff)
sca$s_dawba_bdd_2_sum <- sca %>% select(matches("s_dawba_bdd_2")) %>% rowSums(na.rm=TRUE)
sca2 <- sca %>% filter(s_dawba_bdd_1_concerns_appearance=="2" |
(s_dawba_bdd_1_concerns_appearance=="1" &
(s_dawba_bdd_2a_skin_condition=="2" | s_dawba_bdd_2b_skin_colour=="2" | s_dawba_bdd_2c_hair_colour_or_condition=="2" |
s_dawba_bdd_2d_muscle_bulk=="2" | s_dawba_bdd_2e_body_shape_or_size=="2" |
s_dawba_bdd_2f_facial_features=="2" | s_dawba_bdd_2g_other_body_part=="2" |
s_dawba_bdd_2h_asymmetry=="2" | s_dawba_bdd_2i_other_aspect_of_appearance=="2"))) %>%
mutate(s_dawba_bdd_criterion_a = 1) %>% select(DAWBA_ID, s_dawba_bdd_criterion_a)
sca <- left_join(sca, sca2, all=TRUE) %>% select(DAWBA_ID, PLUSID, Initials, s_dawba_bdd_2_sum, s_dawba_bdd_criterion_a)
# criterion b = repetitive behaviours
scb <- dawba_w_names %>% select(DAWBA_ID, PLUSID, Initials, matches("s_dawba_bdd_4"))
scb[,4:ncol(scb)] <- lapply(scb[,4:ncol(scb)], FUN = function(x) recode(x, `0`=0, `1`=0, `2`=1, .missing = NULL))
scb$no_columns <- scb %>% select(matches('s_dawba_bdd_4')) %>% ncol() %>% as.numeric()
scb$NA_count <- scb %>% select(matches('s_dawba_bdd_4')) %>% apply(., 1, count_na)
scb$diff <- c(scb$no_columns - scb$NA_count)
scb <- scb %>% filter(diff>0) %>% select(-no_columns, -NA_count, -diff)
scb$s_dawba_bdd_4_sum <- scb %>% select(matches("s_dawba_bdd_4")) %>% rowSums(na.rm=TRUE)
scb <- scb %>% mutate(s_dawba_bdd_criterion_b = ifelse(s_dawba_bdd_4_sum>2, 1, NA)) %>%
select(DAWBA_ID, PLUSID, Initials, s_dawba_bdd_4_sum, s_dawba_bdd_criterion_b)
# criterion c = clinically significant distress
scc <- dawba_w_names %>%
select(DAWBA_ID, PLUSID, Initials, s_dawba_bdd_5a_time_spent_worrying_appearance,
s_dawba_bdd_5b_time_spent_hiding_improving_appearance,
s_dawba_bdd_8_distress, s_dawba_bdd_9a_impact_on_family_life,
s_dawba_bdd_9b_impact_on_friendships, s_dawba_bdd_9c_impact_on_learning,
s_dawba_bdd_9d_impact_on_leisure)
scc[,4:5] <- lapply(scc[,4:5], FUN = function(x) recode(x, `0`=0, `1`=0, `2`=0, `3`=3, `4`=3, .missing = NULL))
scc <- scc %>% filter(s_dawba_bdd_5a_time_spent_worrying_appearance=="3" | s_dawba_bdd_5b_time_spent_hiding_improving_appearance=="3" |
s_dawba_bdd_8_distress=="3" | s_dawba_bdd_9a_impact_on_family_life=="3" |
s_dawba_bdd_9b_impact_on_friendships=="3" | s_dawba_bdd_9b_impact_on_friendships=="3" |
s_dawba_bdd_9c_impact_on_learning=="3" | s_dawba_bdd_9d_impact_on_leisure=="3") %>%
mutate(s_dawba_bdd_criterion_c = 1) %>%
select(DAWBA_ID, PLUSID, Initials, s_dawba_bdd_criterion_c)
# recombining
s_bdd_combined <- merge.default(sca, scb, all=TRUE) %>% merge.default(., scc, all=TRUE)
s_bdd_combined$s_dawba_bdd_criterion_a[is.na(s_bdd_combined$s_dawba_bdd_criterion_a)] <- 0
s_bdd_combined$s_dawba_bdd_criterion_b[is.na(s_bdd_combined$s_dawba_bdd_criterion_b)] <- 0
s_bdd_combined$s_dawba_bdd_criterion_c[is.na(s_bdd_combined$s_dawba_bdd_criterion_c)] <- 0
# determining whether a diagnosis is met
s_bdd_combined <- s_bdd_combined %>% mutate(s_dawba_bdd_diag = (as.numeric(s_dawba_bdd_criterion_a) + as.numeric(s_dawba_bdd_criterion_b) + as.numeric(s_dawba_bdd_criterion_c)))
#####
# Integrating the BDD probabilities into the DAWBA database
all_bdd_combined <- merge.default(s_bdd_combined, p_bdd_combined, all=TRUE)
all_bdd_combined <- all_bdd_combined %>% group_by(DAWBA_ID) %>%
fill(., names(all_bdd_combined), .direction = "down") %>%
fill(., names(all_bdd_combined), .direction = "up") %>%
distinct(., .keep_all = TRUE) %>% ungroup()
dawba_w_names <- merge.default(dawba_w_names, all_bdd_combined, all=TRUE)
# Exporting the database --------------------------------------------------
dawba_w_names %>% write_xlsx(paste0(dawba_pull, "old/dawba_archive_", todays_date_formatted, ".xlsx"))
dawba_w_names %>% write_xlsx(paste0(database_location, "MASTER_DATABASE_DAWBA.xlsx"))
# DAWBA deletions ---------------------------------------------------------
# creating lists of DAWBA IDs to delete
dawba_removal <- DAWBA_Data_Download_raw %>% filter(!is.na(logins)) %>%
select(sid, firstcreated, logins, plogins, slogins, psdqdate, ssdqdate) %>%
rename(DAWBA_ID="sid")
dawba_removal$psdqdate <- as.Date(dawba_removal$psdqdate, "%d.%m.%y")
dawba_removal$ssdqdate <- as.Date(dawba_removal$ssdqdate, "%d.%m.%y")
dawba_removal$Overall_date <- coalesce(dawba_removal$ssdqdate, dawba_removal$psdqdate)
dawba_removal <- dawba_removal %>% filter(!is.na(Overall_date))
dawba_removal$since_sdq <- ((difftime(dawba_removal$Overall_date, todays_date_formatted, units = "weeks"))/4) %>% round(., digits = 2)
# SDQ completed > 4 months ago
dawba_removal %>% filter(since_sdq < -5) %>% select(DAWBA_ID, Overall_date, since_sdq) %>%
write_xlsx(paste0(database_location, "dawba_deletions/", "dawba_greater_3m_", todays_date_formatted, ".xlsx"))
# Removing unnecessary variables ------------------------------------------
rm(pca, pca2, pcb, pcc, sca, sca2, scb, scc, split1, s_bdd_combined, p_bdd_combined, participant_identifiers_combined, all_bdd_combined,
master_IRTA_identifiers, master_current_screen_identifiers, master_old_screen_identifiers, DAWBA_Data_Download,
DAWBA_Data_Download_raw, DAWBA_Archive, dawba_combined, dawba_columns, dawba_date_variables, dawba_removal)