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import.R
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import.R
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# imports the pre-cleared Health after COVID (HACT,
# observation time at least 28 days) and CovILD project data.
# Defines common globals, a shared format of the variable dictionaries
# and calculates individual times to recovery for specific symptoms.
# The subset of the HACT study analyzed in the current project has an
# observation time of at least 90 days and with acute symptoms.
library(soucer)
library(plyr)
library(tidyverse)
library(foreign)
library(stringi)
library(trafo)
insert_head()
source_all('./tools/project_tools.R',
message = TRUE, crash = TRUE)
# container lists ------
rec_time <- list()
mod_tbl <- list()
# globals ------
insert_msg('Globals')
source_all('./tools/project_globals.R',
message = TRUE, crash = TRUE)
# unpacking the data files -----
insert_msg('Unpacking')
load('./data/covild.RDa')
load('./data/hact.RDa')
hact[c('north', 'south')] <- hact[c('north', 'south')] %>%
map(filter,
obs_time >= 90,
acute_covid == 'yes') %>%
map(mutate,
comorb_present = ifelse(comorb_absent == 'yes', 'no', 'yes'),
comorb_present = factor(comorb_present, c('no', 'yes')),
incomplete_covelescence = ifelse(complete_covelescence == 'yes',
'no', 'yes'),
incomplete_covelescence = factor(incomplete_covelescence, c('no', 'yes')),
bmi_class_before = car::recode(bmi_class_before,
"'overweigth' = 'overweight'"),
bmi_class_before = factor(bmi_class_before,
c('normal', 'overweight', 'obesity')),
bmi_class_recent = car::recode(bmi_class_recent,
"'overweigth' = 'overweight'"),
bmi_class_recent = factor(bmi_class_recent,
c('normal', 'overweight', 'obesity')))
# Formatting the dictionaries -----
insert_msg('Common format for the variable dictionaries')
covild$dict <- covild$dict %>%
mutate(label_short = label)
hact$dict <- hact$dict %>%
select(variable,
label,
label_short,
unit,
description,
cutpoints,
levels) %>%
rbind(tibble(variable = 'incomplete_covelescence',
label = 'Incomplete recovery',
label_short = 'incomplete recovery',
unit = NA,
description = 'Self-perceived incomplete convalescence',
cutpoints = NA,
levels = 'no, yes')) %>%
rbind(tibble(variable = 'comorb_present',
label = 'Comorbidity',
label_short = 'comorbidity',
unit = NA,
description = 'At least one comorbidity present',
cutpoints = NA,
levels = 'no, yes')) %>%
mutate(axis_lab = ifelse(!is.na(unit),
paste(label_short, unit, sep = ', '),
label_short))
hact$dict[hact$dict$variable == 'new_medication_fup', 'label_short'] <-
'new medication'
hact$dict <- hact$dict %>%
mutate(label = stri_replace(label,
fixed = 'Blue marmorated skin',
replacement = 'Marmorated skin'),
label = stri_replace(label,
fixed = 'Imp. fine motor skills',
replacement = 'Imp. FMS'))
## more informative names for psychometic measures
hact$dict[hact$dict$variable == 'life_quality_score', c('label', 'label_short', 'description', 'axis_lab')] <-
tibble(label = 'QoL impairment score',
label_short = 'QoL impairment score',
description = 'Score of impaired quality of life (QoL, 0: excellent QoL; 3: poor QoL)',
axis_lab = 'QoL impairment score')
hact$dict[hact$dict$variable == 'mental_health_score', c('label', 'label_short', 'description', 'axis_lab')] <-
tibble(label = 'OMH impairment score',
label_short = 'OMH impairment score',
description = 'Score of impaired self-assessed overall mental health (OMH, 0: excellent QoL; 3: poor QoL)',
axis_lab = 'OMH impairment score')
# Calculating the times to recovery, HACT -----
insert_msg('Individual times to recovery, HACT')
rec_time[c('north', 'south')] <- hact[c('north', 'south')] %>%
map(select, ID, all_of(globals$hact_symptoms)) %>%
map(~map_dfc(.x,
car::recode,
"'absent' = 0;
'1 - 3 days' = 3;
'up to 1 week' = 7;
'up to 2 weeks' = 14;
'up to 4 weeks' = 28;
'up to 3 months' = 90;
'up to 6 months' = 90;
'over 6 months' = 90")) %>%
map(~map_dfc(.x, function(x) if(is.factor(x)) as.numeric(as.character(x)) else x))
# Calculating the times to recovery, CovILD ------
insert_msg('Individual times to recovery, CovILD')
## maximal symptom duration, ignoring the remission/relapse
rec_time$covild <- covild$data %>%
select(ID, time, all_of(globals$covild_symptoms)) %>%
blast(time) %>%
map(select, - time) %>%
map2_dfr(., c(14, 60, 90, 180, 360),
~map_dfc(.x,
function(x) if(is.factor(x)) ifelse(x == 'yes', .y, 0) else x)) %>%
blast(ID) %>%
map(~map_dfc(.x,
function(x) if(is.numeric(x)) max(x, na.rm = TRUE) else x)) %>%
map_dfr(~.x[1, ]) %>%
left_join(covild$data %>%
filter(time == 4) %>%
select(ID, cat_WHO),
by = 'ID')
# kinetic modeling tables in long format, HACT -----
insert_msg('Kinetic modeling table with the symptoms, HACT')
## presence of the particular symptom at the 3, 7, 14 and so on
## time points
mod_tbl[c('north', 'south')] <- hact[c('north', 'south')] %>%
map(select, ID, all_of(globals$hact_symptoms))
mod_tbl[c('north', 'south')] <- mod_tbl[c('north', 'south')] %>%
map(function(cohort) cohort %>%
blast(ID) %>%
map(select, -ID) %>%
map(~map_dfc(.x, function(sympt) switch(as.character(sympt),
absent = c(0, 0, 0, 0, 0),
`1 - 3 days` = c(1, 0, 0, 0, 0),
`up to 1 week` = c(1, 1, 0, 0, 0),
`up to 2 weeks` = c(1, 1, 1, 0, 0),
`up to 4 weeks` = c(1, 1, 1, 1, 0),
`up to 3 months` = c(1, 1, 1, 1, 1),
`up to 6 months` = c(1, 1, 1, 1, 1),
`over 6 months` = c(1, 1, 1, 1, 1)))) %>%
map2_dfr(., names(.), ~mutate(.x, ID = .y, time = c(3, 7, 14, 28, 90))))
# kinetic modeling table, COVILD ------
insert_msg('Kinetic modeling table, CovILD')
mod_tbl$covild <- covild$data %>%
select(ID, cat_WHO, time_numeric, all_of(globals$covild_symptoms))
# reading the sniffing stick test results -----
insert_msg('Sniffing stick test results')
sst <- list()
sst$data_100_fup <- read.spss('./data/COVIDPat_135_cluster.sav',
to.data.frame = TRUE) %>%
transmute(ID = stri_replace(pat_id, regex = '\\s+$', replacement = ''),
sniff_score = as.numeric(sniffin_sticks_score),
sniff_score_cut = cut(sniff_score,
c(-Inf, 8, 12, Inf),
c('severe', 'moderate', 'normal')),
sniff_score_cut = factor(sniff_score_cut,
c('normal', 'moderate', 'severe')),
sniff_hyposmia = ifelse(sniff_score < 13, 'yes', 'no'),
sniff_hyposmia = factor(sniff_hyposmia, c('no', 'yes'))) %>%
left_join(covild$data %>%
filter(time == 1) %>%
select(ID, cat_WHO, anosmia_sympt),
.,
by = 'ID') %>%
filter(complete.cases(.))
sst$data_360_fup <- read.spss('./data/ATTRACT_81pts_hyposmia.sav',
to.data.frame = TRUE) %>%
transmute(ID = stri_replace(PatID, regex = '\\s+$', replacement = ''),
sniff_score = as.numeric(sniff_score_1a),
sniff_score_cut = cut(sniff_score,
c(-Inf, 8, 12, Inf),
c('severe', 'moderate', 'normal')),
sniff_score_cut = factor(sniff_score_cut,
c('normal', 'moderate', 'severe')),
sniff_hyposmia = ifelse(sniff_score < 13, 'yes', 'no'),
sniff_hyposmia = factor(sniff_hyposmia, c('no', 'yes'))) %>%
left_join(covild$data %>%
filter(time == 4) %>%
select(ID, cat_WHO, anosmia_sympt),
.,
by = 'ID') %>%
filter(complete.cases(.))
# manual editing of the variable dictionary -------
insert_msg('Manual editing of the variable dictionary')
hact$dict <- hact$dict %>%
mutate(label = ifelse(variable == 'anosmia',
'OD', label),
label_short = ifelse(variable == 'anosmia',
'OD', label_short),
label = ifelse(variable == 'taste_loss',
'Hypogeusia/ageusia', label),
label_short = ifelse(variable == 'taste_loss',
'hypogeusia/ageusia', label_short))
covild$dict <- covild$dict %>%
mutate(label = ifelse(variable == 'anosmia_sympt',
'OD', label),
label_short = ifelse(variable == 'anosmia_sympt',
'OD', label))
# END ----
insert_tail()