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get_plots.Rmd
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---
title: "charity-analysis"
subtitle:"version_004"
author: "PotapenkoEugene"
date: "2022-12-20"
output: html_document
---
# Library install
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
# Load preprocessed data
source("CharityHospital_R_2022-11-02_1448.R")
if(!require('Hmisc')) install.packages('Hmisc') ; library(Hmisc)
if(!require('tidyverse')) install.packages('tidyverse') ; library(tidyverse)
if(!require('data.table')) install.packages('data.table') ; library(data.table)
if(!require('dplyr')) install.packages('dplyr') ; library(dplyr)
if(!require('magrittr')) install.packages('magrittr') ; library(magrittr)
if(!require('eeptools')) install.packages('eeptools') ; library(eeptools)
if(!require('naniar')) install.packages('naniar') ; library(naniar)
if(!require('fastDummies')) install.packages('fastDummies') ; library(fastDummies)
if(!require('forcats')) install.packages('forcats') ; library(forcats)
if(!require('wesanderson')) install.packages('wesanderson') ; library(wesanderson)
if(!require('RColorBrewer')) install.packages('RColorBrewer') ; library(RColorBrewer)
if(!require('ggpubr')) install.packages('ggpubr') ; library(ggpubr)
if(!require('grid')) install.packages('grid') ; library(grid)
if(!require('gridExtra')) install.packages('gridExtra') ; library(gridExtra)
if(!require('ggplotify')) install.packages('ggplotify') ; library(ggplotify)
if(!require('ggmosaic')) install.packages('ggmosaic') ; library(ggmosaic)
if(!require('ggpol')) install.packages('ggpol') ; library(ggpol)
if(!require('plotly')) install.packages('plotly') ; library(plotly)
if(!require('ggplot2')) install.packages('ggplot2') ; library(ggplot2)
if(!require('icesTAF')) install.packages('icesTAF') ; library(icesTAF)
if(!require('grateful')) remotes::install_github("Pakillo/grateful") ; library(grateful)
cite_packages()
PLOTDIR = paste('plots/') ;mkdir(PLOTDIR)
```
# Functions
```{r}
### FUNC
doc_vars_mutate <-
function(df, col.bool, col.reason){
df %>%
dplyr::rename(target.bool = !!col.bool, target.reason = !!col.reason) %>%
# Move some values from "bool" to reason
dplyr::mutate(target.reason = as.factor(case_when(target.bool == 'восстанавливает' ~ target.bool,
target.bool == 'заложен' ~ target.bool,
TRUE ~ target.reason))) %>%
# "bool" to bool
dplyr::mutate(target.bool = as.factor(case_when(target.bool %in% c('есть') ~ T,
target.bool %in% c('восстанавливает',
'заложен',
'нет')~ F,
target.bool == 'нет данных' ~ NA))) %>%
dplyr::rename_with(~ c(col.bool, col.reason), all_of(c('target.bool', 'target.reason')))
}
sum_rowwise_vars <-
function(df, prefix, NAstring){
df %>%
dplyr::filter(is.na(redcap_repeat_instance)) %>% # keep only info rows of records
dplyr::select(record_id, starts_with(prefix)) %>%
dplyr::mutate_at(vars(starts_with(prefix)), function(x) na_if(x, NAstring)) %>% # replace ОТРИЦАЕТ with NA
# Calculate number of ch ds per patient
dplyr::mutate_at(vars(starts_with(prefix)), function(x) ifelse(!is.na(x), T, F)) %>%
rowwise() %T>%
{varname <<- paste0(prefix, '.number')} %>% # save new varname
mutate(!!varname := sum(cur_data()) - record_id) %>% # just minus record_id (simpliest way)
dplyr::select(!!varname)
}
first_last_dinamics <- function(vec){
combinations <-
vec %>%
str_split(., '\\|') %>%
do.call(c, .) %>%
unique %>%
.[. != 'NA'] %>% # drop NA
c(., .) %>%
combn(2) %>%
t %>%
as.data.frame %>%
unique %>%
rowwise() %>%
dplyr::mutate(Rangs = paste0(V1, ' -> ', V2)) %>%
.$Rangs
vec %>%
str_split(., '\\|') %>%
sapply(function(x) {
x = x[x != 'NA']
if(length(x) == 0) {
return(NA)
} else{
x = ifelse(x[1] == x[length(x)],
x[1],
paste0(x[1], ' -> ', x[length(x)]))
}
}
)
}
most_frequent_dinamics <- function(vec){
vec %>%
str_split(., '\\|') %>%
sapply(function(x) {
x = x[x != 'NA']
if(length(x) == 0) {
return(NA)
}else{
median(x)
}
})
}
# Машина функция
get_one_var_hist <- function(data, var, out_dir, xtitle,
pall_values, height, width,
ytitle='Количество человек',
ratio=0.2) {
if (!dir.exists(out_dir)) {
dir.create(out_dir, recursive = T)
}
df <- data.frame(table(data[[var]])) %>%
magrittr::set_colnames(c('condition', 'counts'))
y_lim <- plyr::round_any(max(df$counts) + 100, 100)
plot_title <- sprintf('%s (%d/%d -- есть информация, %d/%d -- NAs)',
xtitle, sum(df$counts), nrow(data),
sum(is.na(data[[var]])), nrow(data))
plt <- ggplot(df, aes(x = reorder(condition, counts), y = counts, fill=condition))+
geom_bar(stat = 'identity')+
geom_text(aes(label = counts), vjust = 0, size=4.5)+
theme_bw(base_size=11)+
ylab(ytitle)+
xlab(xtitle)+
ggtitle(plot_title)+
ylim(0, y_lim)+
scale_fill_manual(values=pall_values)+
theme(legend.text.align = 0, legend.key.size=unit(0.2, "in"),
legend.text = element_text(size=13),
aspect.ratio = ratio, legend.position = 'right',
legend.title = element_blank(),
plot.margin=grid::unit(c(0,0,0,0), "in"),
axis.text.x = element_text(angle = 45, hjust=1),
plot.title = element_text(hjust = 0.5))
ggsave(plt, filename = sprintf('%s/%s_bar_plot.png', out_dir, var),
height = height, width = width)
plt
}
# Classify ICD code to specific intervals
codeICD_to_IntervalICD <- function(ICD){ # --> c(ICD, description)
intervals = c('A00-B99', 'C00-D48', 'D50-D89', 'E00-E90', 'F00-F99', 'G00-G99', 'H00-H59', 'H60-H95', 'I00-I99', 'J00-J99', 'K00-K93', 'L00-L99', 'M00-M99', 'N00-N99', 'O00-O99', 'Q00-Q99', 'S00-T98', 'V01-Y98', 'Z00-Z99')
decoding = c('Некоторые инфекционные и паразитарные болезни',
'Новообразования',
'Болезни крови, кроветворных органов и отдельные нарушения, вовлекающие иммунный механизм',
'Болезни эндокринной системы, расстройства питания и нарушения обмена веществ',
'Психические расстройства и расстройства поведения',
'Болезни нервной системы',
'Болезни глаза и его придаточного аппарата',
'Болезни уха и сосцевидного отростка',
'Болезни системы кровообращения',
'Болезни органов дыхания',
'Болезни органов пищеварения',
'Болезни кожи и подкожной клетчатки',
'Болезни костно-мышечной системы и соединительной ткани',
'Болезни мочеполовой системы',
'Беременность, роды и послеродовой период',
'Врожденные аномалии [пороки развития], деформации и хромосомные нарушения',
'Травмы, отравления и некоторые другие последствия воздействия внешних причин',
'Внешние причины заболеваемости и смертности',
'Общий осмотр и обследование лиц, не имеющих жалоб или установленного диагноза')
# return NA on NA
if(ICD == 'NA' | is.na(ICD) | ICD == '') {return ('NA')}
# Process code
ICD %<>% gsub('\\.[0-9]+', '', .)
letter = substr(ICD, 1, 1)
num = as.numeric(substr(ICD, 2, 3))
answer = intervals[grepl(letter, intervals)]
# special cases D and H
if(letter == 'D'){ answer <- ifelse(num < 50, 'C00-D48', 'D50-D89')}
if(letter == 'H'){ answer <- ifelse(num < 60, 'H00-H59', 'H60-H95')}
if(length(answer) == 0){answer <- NA}
return(answer)
}
# Required df with value column
decode.ICD <- function(df) { df %>% dplyr::mutate(value = as.factor(case_when(value == 1 ~ 'A00-B99',
value == 2 ~ 'C00-D48',
value == 3 ~ 'D50-D89',
value == 4 ~ 'E00-E90',
value == 5 ~ 'F00-F99',
value == 6 ~ 'G00-G99',
value == 7 ~ 'H00-H59',
value == 8 ~ 'H60-H95',
value == 9 ~ 'I00-I99',
value == 10 ~ 'J00-J99',
value == 11 ~ 'K00-K93',
value == 12 ~ 'L00-L99',
value == 13 ~ 'M00-M99',
value == 14 ~ 'N00-N99',
value == 15 ~ 'O00-O99',
value == 16 ~ 'Q00-Q99',
value == 17 ~ 'S00-T98',
value == 18 ~ 'V01-Y98',
value == 19 ~ 'Z00-Z99')))
}
# Function for ordinal encoding
encode_ordinal <- function(x, order = sort(unique(x))) {
x <- as.numeric(factor(x, levels = order, exclude = NULL))
as.factor(x)
}
```
# Postprocessing
```{r warning=F}
data <-
data %>%
# Rename some vars
dplyr::rename(Observation = redcap_repeat_instrument.factor) %>%
# Drop useless vars
dplyr::select(-(starts_with('complaint_lite') & ends_with('.factor'))) %>%
# Split some vars
separate(id_status.factor, into = c('id_status.factor.bool', 'id_status.factor.reason'), sep = '/') %>%
separate(oms_status.factor, into = c('oms_status.factor.bool', 'oms_status.factor.reason'), sep = '/') %>%
separate(sn_status.factor, into = c('sn_status.factor.bool', 'sn_status.factor.reason'), sep = '/') %>%
# Mutate docs vars
doc_vars_mutate('id_status.factor.bool', 'id_status.factor.reason') %>%
doc_vars_mutate('oms_status.factor.bool', 'oms_status.factor.reason') %>%
doc_vars_mutate('sn_status.factor.bool', 'sn_status.factor.reason') %>%
# Encode homeless
dplyr::mutate(Homeless = case_when(where_homless %in% c(1, 14, 16, 6, 11) ~ 'уличный',
where_homless %in% c(17, 2, 3, 4, 12, 5, 15, 7) ~ 'условно уличный',
where_homless %in% c(8, 9, 13) ~ 'домашний',
where_homless == 10 | is.na(where_homless) ~ as.character(NA))) %>%
# create SMP variable
dplyr::mutate(smp = case_when(trimws(place) %in% c('14 городская больница', '36 ГБ', '40', '40 ГБ, Сестрорецк', 'александровская больница', 'Боткина, уже там, старая', 'ГБ 14', 'ГБ 40', 'ГБ №3', 'ГБ №40', 'Георгия', 'ГКБ №40', 'Джанелидзе', 'Мариинская больница 5 отд. 11 палата', 'Покровская больница', 'Попытка госпитализации в ГНБ. Отказ от госпитализации по причине отсутствия прожарки и справки БОМЖ. В приёмном покое ГНБ у пациента возникли судороги. Вызвана скорая помощь, доставлен Александровскую больницу', 'СМП', 'Смп 78 Леванеев бригада,Джа', 'Травма по 55 гп 112 СМП', 'НИИ кардиологии им Алмазова', 'отказ от госпитализации') ~ T,
is.na(place) ~ NA,
T ~ F)) %>%
dplyr::mutate(where.category =
case_when(where %in% c(1) ~ 'ночной приют',
where %in% c(2,16,20,19) ~ 'приют',
where %in% c(9,8,7,6,5,4,3,21,22,14,23) ~ 'стоянка',
where %in% c(11,10,12) ~ 'пункт обогрева',
where %in% c(15) ~ 'медицинский центр',
where == 13 ~ 'удаленная консультация'
)) %>%
dplyr::rename(value = ds_icd_1) %>% decode.ICD %>% dplyr::rename(ds_icd_1 = value) %>%
dplyr::rename(value = ds_icd_2) %>% decode.ICD %>% dplyr::rename(ds_icd_2 = value) %>%
dplyr::rename(value = ds_icd_3) %>% decode.ICD %>% dplyr::rename(ds_icd_3 = value)
# process diagnose variable
data$ds.processed <-
data$ds %>%
trimws %>% # strip
tolower %>% # lower register
# remove dates and ?
gsub('(от)?[ ]?[0-9]+\\.[0-9]+[\\.0-9]*[г\\.\\)]?|\\?', '', .) %>%
# replace dots, commas and newline with space
gsub('\\.|,|\n', ' ', .) %>%
# remove multiple spaces
gsub('[ ]+', ' ', .) %>%
# Add space in the begining
gsub('^', ' ', .) %>%
# replace most frequent abbreviations
gsub(' гб ', ' гипертоническая болезнь ', .) %>%
gsub(' cco | ссо4 ', ' сердечно-сосудистых осложнений ', .) %>%
gsub(' аг | эаг ', ' артериальная гипертензия ', .) %>%
gsub(' ибс ', ' ишемическая болезнь сердца ', .) %>%
gsub(' онмк | ии ', ' инсульт ', .) %>%
gsub(' отит ', ' Воспаление уха ', .) %>%
gsub(' ддзп ', ' остеохондроз ', .) %>%
gsub(' арвт ', ' антиретровирусная терапия ', .) %>%
gsub(' гэрб ', ' изжога ', .) %>%
gsub(' пресбиопия ', ' дальнозоркость ', .) %>%
gsub(' цвб ', ' цереброваскулярная болезнь ', .) %>%
gsub(' нк ', ' нижняя конечность ', .) %>%
gsub(' врвнк ', ' варикозно расширенные вены нижней конечности ', .) %>%
gsub(' мос ', ' металлоостеосинтез ', .) %>%
gsub(' сд ', ' сахарный диабет ', .) %>%
gsub(' поп ', ' поясничный отдел позвоночника ', .) %>%
gsub(' хобл ', ' хроническая обструктивная болезнь легких ', .) %>%
gsub(' туб ', ' туберкулез ', .) %>%
gsub(' тбс ', ' тазобедренный сустав ', .) %>%
gsub(' хсн ', ' хроническая сердечная недостаточность ', .) %>%
gsub(' гсс ', ' голеностопный сустав ', .) %>%
gsub(' кса ', ' капсульно-связочный аппарат ', .) %>%
gsub(' нки ', ' коронавирусная инфекция ', .) %>%
gsub(' соп ', ' сопутсвующее ', .) %>%
gsub(' дгжп ', ' доброкачественная гиперплазия предстательной железы ', .) %>%
gsub(' дэ ', ' дисциркуляторная энцефалопатия ', .) %>%
gsub(' жда ', ' железодефицитная анемия ', .) %>%
gsub(' фрж | фржкт ', ' функциональное расстройство желудочно-кишечного тракта ', .) %>%
gsub(' ябж ', ' язвенная болезнь желудка ', .) %>%
gsub(' ба ', ' бронхиальная астма ', .) %>%
gsub(' зчмт ', ' закрытая черепно-мозговая травма ', .) %>%
gsub(' жкб ', ' желчнокаменная болезнь ', .) %>%
gsub(' 12пк ', ' двенадцатиперстной кишки ', .) %>%
gsub(' гепс ', ' гепатит C ', .) %>%
trimws %>%
as.character
# Load diagnosis decoded with chatGPT
data <-
readxl::read_xlsx('Decoded_ds.xlsx') %>%
dplyr::select(ds.processed, text.intervals, text) %>%
dplyr::rename(ds.ICD = text,
ds.ICD.intervals = text.intervals) %>%
left_join(data, ., by = 'ds.processed')
```
# chatGPT decoding diagnoses
```{r}
# chatGPT decoding of diagnosis in ICD-10 code
if(F){ # skip by default
# DS
ds <- data$ds.processed %>% na.omit %>% unique
# library(gptchatteR)
# Auth
key = '...'
chatter.auth(key) # need personal API key
# Create chat
chatter.create()
# Chatting!
context = 'Раздели составные врачебные диагнозы написанные в свободном стиле на отдельные диагнозы и переведи их в классификацию по МКБ-10. Ответ запиши в виде кодов МКБ-10 перечисленных через запятую. При невозожности определения диагноза к одной из классификаций МКБ-10, заполни как пропущенное значение: NA'
chatter.feed(context)
chunks = c(seq(from = 20, to = length(ds), by = 20), length(ds))
answers.list <-
lapply(1:length(chunks), function(i){
if(i==1){
start = 1
end = chunks[i]
} else{
start = chunks[i-1] + 1
end = chunks[i]
}
answer = chatter.chat(ds[start:end],return_response = T, feed = F)
Sys.sleep(5)
return(answer$choices)
})
library(xlsx)
ds.df <- data.frame(ds.processed = ds)
answers.df <-
answers.list %>%
do.call(rbind, .) %>%
cbind(ds.df, .) %>%
dplyr::mutate(text = gsub('\n', '', trimws(text))) %T>%
# Save raw answers
write.xlsx('Decoding_diagnosis.xlsx') %>%
# Process answers
# Let's mark all answers with words (not correct)
dplyr::mutate(incorrect = grepl('[[:lower:]]+', text))
answers.df <-
readxl::read_xlsx('Decoding_diagnosis.xlsx') %>%
dplyr::mutate(incorrect = grepl('[[:lower:]]+', text))
# Convert in interval (only for correct answers)
answers.df %>%
dplyr::filter(incorrect) %>%
write.xlsx('Decoding_diagnosis_incorrect.xlsx')
decode.correct <-
answers.df %>%
dplyr::filter(!incorrect) %>%
dplyr::mutate(text.intervals = sapply(text, function(x){
str_split(x, ',') %>% unlist %>% trimws %>%
sapply(codeICD_to_IntervalICD) %>%
unique %>% #TODO make var with repeats!
paste(collapse = ',')
})
) %>%
dplyr::select(ds.processed, text.intervals, text, incorrect) %T>%
write.table('Decoding_diagnosis_full.tsv', sep = '\t', row.names = F)
decoded.incorrect <- # corrected manually
readxl::read_xlsx('Decoding_diagnosis_incorrect_CORRECTED.xlsx') %>%
dplyr::select(ds.processed, text.intervals, text, incorrect)
rbind(decode.correct, decoded.incorrect) %>%
write.xlsx('Decoded_ds.xlsx')
}
```
# EDA
```{r}
# Look on lethal cases
data$ds[grepl('смерть|летальный', data$ds, ignore.case = T)] # only 4 cases
```
# Classified vars
```{r}
# Base vars:
base_vars.factor = c('gender.factor', 'age.group',
# Docs
'id_status.factor.reason', 'oms_status.factor.reason', 'sn_status.factor.reason',
# Additctions
'nicotin.factor', 'alcogolic.factor', 'narco.factor', 'ne_narco.factor',
'hiv_1.factor', 'lues.factor', 'hb_1.factor', 'hc_1.factor',
'tbi.factor', 'mls.factor')
base_vars.bool = c('id_status.factor.bool', 'oms_status.factor.bool', 'sn_status.factor.bool')
quant_vars = c('ObsNum', 'age.actual', 'ds.ICD.intervals.N')
# Dummy variables:
dummy_vars_raw = c('Observation', 'Homeless',
'ds_icd_1.factor', 'ds_icd_2.factor', 'ds_icd_3.factor',
'etest_hiv.factor', 'etest_hbsag.factor', 'etest_hcv.factor', 'etest_lues.factor', 'etest_covid19.factor')
dummy_vars_already <- c('complaint_lite')
# Dinamic variables
dinamic_vars = c('Observation', 'Homeless', 'alc_status.factor', 'smp', 'where.category', 'glu', 'ds_icd_1', 'ds_icd_2', 'ds_icd_3')
sequence_vars = c('ds.ICD.intervals')
```
# Transform data
```{r warning = F}
## Transform BirthDate to actual age (26.11.2022)
data$age.actual[!is.na(data$date_bd)] <-
age_calc(as.Date(data$date_bd[!is.na(data$date_bd)]),
Sys.Date(),
units = 'years') %>%
floor
# Age group
data <-
data %>%
dplyr::mutate(
age.group = case_when(
age.actual < 18 ~ "<18 (несовершеннолетние)",
age.actual >= 18 & age.actual < 45 ~ "18-44 (молодой возраст)",
age.actual >= 45 & age.actual < 60 ~ "45-59 (средний возраст)",
age.actual >= 60 & age.actual < 75 ~ "60-74 (пожилой возраст)",
age.actual >= 75 ~ "75+ (старческий возраст)"
)
)
data$age.group <- factor(data$age.group, levels = sort(unique(data$age.group)))
```
# Processing of each type of vars
```{r warning=F}
# Dinamic vars (should be processed before dummy)
dinamic <-
data %>%
dplyr::select(record_id, dinamic_vars) %>%
dplyr::group_by(record_id) %>%
dplyr::summarise_all(function(x) paste(x, collapse = '|'))
## First - last
dinamic_firstlast <-
dinamic %>%
dplyr::rename_all(function(x) ifelse(x != 'record_id', paste0(x, '.dinamic'), x)) %>%
dplyr::mutate_at(vars(ends_with('.dinamic')),
function(x) first_last_dinamics(x) %>% replace_na('Нет данных') %>% as.factor)
## Most frequent
dinamic_mostfreq <-
dinamic %>%
dplyr::rename_all(function(x) ifelse(x != 'record_id', paste0(x, '.mostfreq'), x)) %>%
dplyr::mutate_at(vars(ends_with('.mostfreq')),
function(x) most_frequent_dinamics(x) %>% replace_na('Нет данных') %>% as.factor)
# Save raw
dinamic <-
dinamic %>%
setNames(c('record_id', paste0(colnames(dinamic)[-1], '.raw')))
## Merge
data_dinamic <-
dinamic %>%
left_join(dinamic_mostfreq, by = 'record_id') %>%
left_join(dinamic_firstlast, by = 'record_id')
# Renew names of dinamic vars with dinamic suffix
dinamic_vars = c(paste0(dinamic_vars, '.dinamic'),
paste0(dinamic_vars, '.mostfreq'),
paste0(dinamic_vars, '.raw'))
# Sequence vars
data_seq <-
data %>%
dplyr::select(record_id, sequence_vars) %>%
dplyr::group_by(record_id) %>%
dplyr::summarise(ds.ICD.intervals.seq = paste(ds.ICD.intervals, collapse = ',')) %>%
# remove NA
dplyr::mutate(ds.ICD.intervals.seq =
gsub('NA,|,NA', '', ds.ICD.intervals.seq)) %>%
# remove duplicates
dplyr::mutate(ds.ICD.intervals.seq =
sapply(ds.ICD.intervals.seq,
function(x){
x %>%
str_split(',') %>%
unlist %>%
unique %>%
paste(collapse = ',')
}) %>% unname
) %>%
na_if('NA') %>%
dplyr::mutate(ds.ICD.intervals.N = ds.ICD.intervals.seq %>%
str_split(',') %>% sapply(length)
)
sequence_vars = paste0(sequence_vars, '.seq')
# Proccess dummy vars (drop original dummy vars)
data_dummy <-
data %>%
dummy_cols(remove_first_dummy = F, ignore_na = T,
select_columns = dummy_vars_raw) %>%
dplyr::select(-dummy_vars_raw) %>% # remove original vars
# Rename dummy vars already (for further join with original df)
dplyr::rename_at(vars(starts_with(dummy_vars_already)), ~paste0('.',.)) %>%
dplyr::select(record_id, starts_with(c(dummy_vars_raw, paste0('.', dummy_vars_already)), ignore.case = F)) %>% # select new vars + already dumm vars
dplyr::group_by(record_id) %>%
dplyr::summarise_all(function(x) sum(x, na.rm =T)) %>%
dplyr::ungroup() %>%
dplyr::select_if(function(x) sum(x, na.rm = T) != 0) # drop zero sum
# Add new dummied variables to list of quant vars
dummy_quant_vars = data_dummy %>% colnames %>% .[-1] # drom record_id
# Process quantative vars (among observation)
data_collapse <-
data %>%
dplyr::group_by(record_id) %>%
dplyr::summarise(ObsNum = ifelse(n() == 1, 1, n() - 1))
# Rowwise vars
data_rowwise <-
data %>%
dplyr::filter(is.na(Observation)) %>% # Keep only informative rows
dplyr::select(record_id) %>%
cbind(sum_rowwise_vars(data, 'ch_ds', 'отрицает')) # bind ID with rowwise vars
# Add rowwise new variables to quant
quant_vars = c(quant_vars, data_rowwise %>% dplyr::select(ends_with('.number')) %>% colnames)
```
# Combine all in tidy df
```{r}
selected_vars <- c(base_vars.bool, base_vars.factor, quant_vars, dummy_quant_vars, dinamic_vars, sequence_vars)
##
data_done <-
data %>%
# filter out rows without base info
dplyr::filter(is.na(redcap_repeat_instrument)) %>%
# Join with summed dummy vars
right_join(data_dummy, by = 'record_id') %>%
# Join with quant vars
right_join(data_collapse, by = 'record_id') %>%
# Join with rowwise vars
right_join(data_rowwise, by = 'record_id') %>%
# Join with dinamic vars
right_join(data_dinamic, by = 'record_id') %>%
# Join with sequence vars
right_join(data_seq, by = 'record_id') %>%
dplyr::select(record_id, all_of(selected_vars))
```
# Add some variable (from several existed)
```{r warning=F}
# Alco
alc_evidence1 = grepl('на приеме чувствуется запах алкоголя|состояние измененного сознания',
data_done$alc_status.factor.raw)
alc_evidence2 = grepl('есть в настоящее время',
data_done$alcogolic.factor)
data_done$alc = alc_evidence1 | alc_evidence2
# Combine social diseases and tests
data_done$hiv.bool = ifelse(data_done$hiv_1.factor %in% c('есть, со слов', 'диспансерный учет в ЦС'), T, F) | data_done$etest_hiv.factor_положительный
data_done$hb.bool = ifelse(data_done$hb_1.factor %in% c('есть, со слов', 'диспансерный учет в ЦС'), T, F) | data_done$etest_hbsag.factor_положительный
data_done$hc.bool = ifelse(data_done$hc_1.factor %in% c('есть, со слов', 'диспансерный учет в ЦС'), T, F) | data_done$etest_hcv.factor_положительный
data_done$lues.bool = ifelse(data_done$lues.factor == 'болел/лечился в диспансере/стационаре', T, F) | data_done$etest_lues.factor_положительный
# Diabet add
data_done$glu <-
data_done$glu.raw %>%
str_split('\\|') %>%
lapply(function(x) {
y = x %>% na_if('NA') %>% as.numeric %>% max(na.rm = T)
y > 11.1
}) %>%
unlist
# ICD variable combining
data_done %<>%
dplyr::mutate(ds_icd.raw = paste(ds_icd_1.raw, ds_icd_2.raw, ds_icd_3.raw, sep = '|') %>%
gsub('NA\\||\\|NA', '', .) %>% na_if('NA') %>% gsub('\\|', ',', .)) %>%
dplyr::mutate(DS = paste(ds.ICD.intervals.seq, ds_icd.raw, sep = ',') %>% str_split(',') %>% sapply(function(x) x %>% trimws %>% unique %>% paste(collapse = ',')) %>% na_if('NA') %>% gsub('NA,', '', .)) %>%
dplyr::mutate(DS.N = DS %>% str_split(',') %>% lapply(function(x) {
if(any(is.na(x))) {return(NA)}
else{return(length(x))}
}) %>% unlist)
# SMP sum
data_done$smp.bool <-
data_done$smp.raw %>%
str_split('\\|') %>%
lapply(function(x) {
xx = x %>% unique %>% na_if('NA')
if(length(xx) == 1 & all(is.na(xx))) {return(NA)}
'TRUE' %in% xx
}) %>% unlist
# MLS
data_done %<>%
dplyr::mutate(mls.bool = replace_na(mls.factor, 'нет') == 'да')
```
# HandMade analyses
## AgeGender
```{r fig.width=8, message=F}
data_done %>%
dplyr::filter(!is.na(gender.factor) & !is.na(age.group)) %>%
dplyr::group_by(age.group, gender.factor) %>%
count %>%
ggplot(aes(n, fct_rev(age.group), fill = gender.factor )) +
geom_bar(stat = 'identity', position = 'dodge') +
labs(x = 'Patients', y = 'Age group', fill = 'Gender') +
scale_fill_manual(values = wes_palettes$Rushmore1[c(3,4)]) +
theme_bw() +
theme(axis.title = element_text(face = 'bold', size = 14),
legend.title = element_text(face = 'bold', size = 14),
axis.text = element_text(size = 12),
legend.text = element_text(size = 12))
ggsave(paste0(PLOTDIR, 'Age_group.png'))
data_done %>%
dplyr::mutate(age.bins = cut(data_done$age.actual, 15),
count = 1) %>%
dplyr::select(age.bins, count, gender.factor) %>%
aggregate(count ~ gender.factor + age.bins, data = ., length) %>%
dplyr::mutate(count = ifelse(gender.factor == 'мужской', count * -1, count)) %>%
#Plot
ggplot(aes(age.bins, count, fill = gender.factor)) +
geom_bar(stat = 'identity') +
facet_share(~gender.factor, dir = 'h', scales = 'free', reverse_num = T) +
coord_flip() +
labs(y = 'Age', x = 'Patients', fill = 'Sex') +
scale_fill_manual(values = wes_palettes$Rushmore1[c(3,4)]) +
theme_bw() +
theme(axis.title = element_text(face = 'bold', size = 14),
legend.title = element_text(face = 'bold', size = 14),
axis.text = element_text(size = 12),
legend.text = element_text(size = 12),
strip.text.x = element_text(face = 'bold', size = 14))
ggsave(paste0(PLOTDIR, 'Age_group2.png'))
```
## Citizenship
```{r message = F}
## citizenship (by Masha)
get_one_var_hist(data=data %>%
filter(`citizen.factor` != 0) %>%
mutate(`citizen.factor` = as.character(`citizen.factor`)),
var='citizen.factor', out_dir='plots',
xtitle='Гражданство', pall_values=c(wes_palettes$Rushmore1, wes_palettes$GrandBudapest1,
wes_palettes$GrandBudapest2),
height = 6, width = 8, ratio=0.8)
```
## Home
### Home overall
```{r message=F}
data %>%
dplyr::filter(!is.na(redcap_repeat_instrument)) %>%
dplyr::select(record_id, redcap_repeat_instrument, Homeless) %>%
dplyr::summarise(Count = table(Homeless, exclude = NULL),
Homeless = names(table(Homeless, exclude = NULL))) %>%
dplyr::mutate(Homeless = case_when(is.na(Homeless) ~ 'Нет данных',
T ~ Homeless)) %>%
# Plot
ggplot(aes(Count, fct_reorder(Homeless, Count) , fill = Homeless)) +
geom_bar(stat = 'identity') +
scale_fill_manual(values = wes_palettes$Rushmore1[2:5]) +
theme_bw() +
theme(axis.title = element_text(face = 'bold', size = 14),
legend.title = element_text(face = 'bold', size = 14),
axis.text = element_text(size = 12),
legend.text = element_text(size = 12),
legend.position = 'none') +
labs(x = 'Observation', y = 'Home status')
ggsave(paste0(PLOTDIR, 'Homeless_category.png'))
```
## Diagnosis
### vs Addictions
```{r warning = F}
ds.other <- data.frame(ds = data_done$DS %>% str_split(',') %>% unlist,
alc = rep(data_done$alc, data_done$DS.N %>% replace_na(1)),
glu = rep(data_done$glu, data_done$DS.N %>% replace_na(1)),
home = rep(data_done$Homeless.mostfreq, data_done$DS.N %>% replace_na(1)),
where = rep(data_done$where.category.mostfreq, data_done$DS.N %>% replace_na(1)),
smp = rep(data_done$smp.bool, data_done$DS.N %>% replace_na(1))
)
df <- data_done %>% dplyr::select(DS, alc) %>% na.omit
names.ds <- data_done$DS %>% str_split(',') %>% unlist %>% na.omit %>% unique
chisq.lst <-
lapply(names.ds, function(dsname){
mask = grepl(dsname, df$DS)
data.frame(state = c(rep('One', df$alc[mask] %>% length),
rep('Other', df$alc[!mask] %>% length)),
alc = c(df$alc[mask], df$alc[!mask])) %>%
table %>%
chisq.test()
})
names(chisq.lst) <- names.ds
chisq.lst
bonf = 0.05/length(names.ds)
```
### vs Where
```{r message =F, fig.width=8, fig.height=6}
ds.other %>%
dplyr::filter(!is.na(ds)) %>%
dplyr::group_by(ds, where) %>%
dplyr::summarise(where.N = n()) %>% dplyr::ungroup() %>%
# Plot
ggplot(aes(fct_reorder(ds, where.N,.desc = T), where.N)) +
facet_grid(rows = vars(where)) +
geom_bar(stat = 'identity', fill = wes_palettes$Rushmore1[4]) +
theme_bw() +
labs(x = 'Заболевания по МКБ-10', y = 'Количество посещений в данной категории',) +
theme(axis.title = element_text(face = 'bold', size = 12),
legend.title = element_text(face = 'bold', size = 12),
axis.text = element_text(size = 12),
axis.text.x = element_text(angle = 45, vjust = 0.6),
legend.text = element_text(size = 7))
ggsave(paste0(PLOTDIR, 'ICD_vs_Where.png'))
```
### vs Home
```{r message=F, fig.width=8, fig.height=6}
ds.other %>%
dplyr::filter(!is.na(ds)) %>%
dplyr::group_by(ds, home) %>%
dplyr::summarise(home.N = n()) %>% dplyr::ungroup() %>%
# Plot
ggplot(aes(fct_reorder(ds, home.N,.desc = T), home.N)) +
facet_grid(rows = vars(home)) +
geom_bar(stat = 'identity', fill = wes_palettes$Rushmore1[3]) +
theme_bw() +
labs(x = 'Заболевания по МКБ-10', y = 'Количество посещений в данной категории') +
theme(axis.title = element_text(face = 'bold', size = 12),
legend.title = element_text(face = 'bold', size = 12),
axis.text = element_text(size = 12),
axis.text.x = element_text(angle = 45, vjust = 0.6),
legend.text = element_text(size = 7))
ggsave(paste0(PLOTDIR, 'ICD_vs_Homeless.png'))
# Chisq
df <- data_done %>% dplyr::select(DS, Homeless.mostfreq) %>% na.omit
chisq.lst <-
lapply(names.ds, function(dsname){
mask = grepl(dsname, df$DS)
data.frame(state = c(rep('One', df$Homeless.mostfreq[mask] %>% length),
rep('Other', df$Homeless.mostfreq[!mask] %>% length)),
alc = c(df$Homeless.mostfreq[mask], df$Homeless.mostfreq[!mask])) %>%
table %>%
chisq.test()
})
names(chisq.lst) <- names.ds
chisq.lst
bonf = 0.05/length(names.ds)
```
### vs Diabet
```{r warning = F}
chisq.test(ds.other$ds, ds.other$glu)
chisq.lst <-
lapply(ds.other$ds %>% table %>% names, function(interval){
df = ds.other[ds.other$ds == interval,] %>% dplyr::select(ds, glu) %>% na.omit
if(sum(df$glu) == 0) {return(NULL)}
chisq.test(df$glu)
})
names(chisq.lst) <- ds.other$ds %>% table %>% names
chisq.lst
```
### vs HIV
```{r warning = F}
df <- data_done %>% dplyr::select(DS, hiv.bool) %>% na.omit
chisq.lst <-
lapply(names.ds, function(dsname){
mask = grepl(dsname, df$DS)
data.frame(state = c(rep('One', df$hiv.bool[mask] %>% length),
rep('Other', df$hiv.bool[!mask] %>% length)),
alc = c(df$hiv.bool[mask], df$hiv.bool[!mask])) %>%
table %>%
chisq.test()
})
names(chisq.lst) <- names.ds
chisq.lst
bonf = 0.05/length(names.ds)
```
### vs SMP
```{r fig.width=10}
ds.other %>%
dplyr::select(ds, smp) %>%
dplyr::filter(smp) %>%
ggplot(aes(y = fct_rev(fct_infreq(ds)))) +
geom_bar(fill = wes_palettes$Rushmore1[3]) +
labs(x = 'Категории', y = 'Количество') +
theme(axis.text.x = element_text(angle = 45)) +
theme(axis.title = element_text(face = 'bold', size = 12),
legend.title = element_text(face = 'bold', size = 12),
axis.text = element_text(size = 12),
axis.text.x = element_text(angle = 45, vjust = 0.6),
legend.text = element_text(size = 7))
ggsave(paste0(PLOTDIR, 'ICD_of_SMP.png'))
ds.other %>%
dplyr::select(ds, smp) %>%
dplyr::mutate(smp = smp %>% replace_na(FALSE)) %>%
na.omit %>%
dplyr::filter(!smp) %>%
ggplot(aes(y = fct_rev(fct_infreq(ds)))) +
geom_bar(fill = wes_palettes$Rushmore1[4]) +
labs(x = 'Категории', y = 'Количество') +
theme(axis.text.x = element_text(angle = 45)) +
theme(axis.title = element_text(face = 'bold', size = 12),
legend.title = element_text(face = 'bold', size = 12),
axis.text = element_text(size = 12),
axis.text.x = element_text(angle = 45, vjust = 0.6),
legend.text = element_text(size = 7))
ggsave(paste0(PLOTDIR, 'ICD_of_nonSMP.png'))
df <-
data_done %>%
dplyr::select(alc, smp.bool)
df %>% table
df %>% table %>% chisq.test()
df <-
data_done %>%
dplyr::select(smp.bool, Homeless.mostfreq) %>%
dplyr::mutate(smp.bool = smp.bool %>% replace_na(FALSE))
df %>% table
df %>% table %>% chisq.test
```
## Social anamnes
```{r}
data_done$mls.bool %>% table
df <-
data_done %>%
dplyr::select(alc, mls.bool)
df %>% table
chisq.test(df %>% table)
```
## Portrait
```{r}
data_done$alc %>% sum / length(data_done$alc)
((data_done$gender.factor == 'мужской') %>% sum(na.rm = T)) / (!is.na(data_done$gender.factor)) %>% sum
(data_done$sn_status.factor.bool %>% as.logical %>% sum(na.rm = T)) / nrow(data_done)
(data_done$id_status.factor.bool %>% as.logical %>% sum(na.rm = T)) / nrow(data_done)
(data_done$oms_status.factor.bool %>% as.logical %>% sum(na.rm = T)) / nrow(data_done)
((data_done$age.group == '45-59 (средний возраст)') %>% sum(na.rm = T)) / (!is.na(data_done$age.group)) %>% sum
(data_done$hiv.bool %>% sum(na.rm = T)) / nrow(data_done)
(data_done$hc.bool %>% sum(na.rm = T)) / nrow(data_done)
(data_done$lues.bool %>% sum(na.rm = T)) / nrow(data_done)
(data_done$tbi.factor %in% names(data_done$tbi.factor %>% table)[1:3]) %>% sum(na.rm = T) / nrow(data_done)
(data_done$glu %>% sum) / nrow(data_done)
(data_done$DS %>% str_split(',') %>% sapply(function(x) 'I00-I99' %in% x) %>% sum(na.rm=T)) / nrow(data_done)
(data_done$Homeless.mostfreq == 'домашний') %>% sum(na.rm = T) / nrow(data_done %>% dplyr::filter(Homeless.mostfreq != 'Нет данных'))
(data_done$Homeless.mostfreq == 'условно уличный') %>% sum(na.rm = T) / nrow(data_done %>% dplyr::filter(Homeless.mostfreq != 'Нет данных'))
(data_done$Homeless.mostfreq == 'уличный') %>% sum(na.rm = T) / nrow(data_done %>% dplyr::filter(Homeless.mostfreq != 'Нет данных'))
((data$where.category == 'стоянка') %>% sum(na.rm = T)) / nrow(data %>% dplyr::filter(!is.na(where.category)))
((data$family.factor == 'состоит в браке') %>% sum(na.rm = T)) / nrow(data %>% dplyr::filter(!is.na(family.factor)))
((data$education.factor == 'высшее, н/высшее') %>% sum(na.rm = T)) / nrow(data %>% dplyr::filter(!is.na(education.factor)))
((data$education.factor == 'средне-специальное') %>% sum(na.rm = T)) / nrow(data %>% dplyr::filter(!is.na(education.factor)))
((data$citizen.factor == 'Россия') %>% sum(na.rm = T)) / nrow(data %>% dplyr::filter(!is.na(citizen.factor)))
```
## ObsNum
```{r}
wilcox.test(data_done$ObsNum[data_done$glu],
data_done$ObsNum[!data_done$glu])
data_done %>%
ggplot(aes(ObsNum, fill = glu)) +
geom_boxplot()
ggsave(paste0(PLOTDIR, 'Diabetics_ObsNum.png'))
wilcox.test(data_done$ObsNum[data_done$hiv.bool],
data_done$ObsNum[!data_done$hiv.bool])
data_done %>%
ggplot(aes(ObsNum, fill = hiv.bool)) +
geom_boxplot()
ggsave(paste0(PLOTDIR, 'HIV_ObsNum.png'))
```
## Tuber
```{r message=F, fig.height=6}
data_done %>%
dplyr::select(Homeless.mostfreq, tbi.factor) %>%
dplyr::mutate(tbi.factor = tbi.factor %>% as.character %>% replace_na('нет данных') %>% encode_ordinal) %>%
dplyr::group_by(Homeless.mostfreq, tbi.factor) %>%
dplyr::summarise(N = n()) %>%
ggplot(aes(tbi.factor, N)) +
facet_grid(rows = vars(Homeless.mostfreq)) +
geom_bar(stat = 'identity', fill = wes_palettes$Rushmore1[3]) +
theme_bw() +
labs(x = 'Категории', y = 'Количество визитов') +
theme(axis.title = element_text(face = 'bold', size = 12),
legend.title = element_text(face = 'bold', size = 12),
axis.text = element_text(size = 12),
axis.text.x = element_text(angle = 45, vjust = 0.6),
legend.text = element_text(size = 7))
ggsave(paste0(PLOTDIR, 'Tub_vs_Homeless.png'))
```
## Diabet
```{r message =F, fig.height=10}