## Article 1 data set definition
## To be enriched from Statistics Denmark
## 
## Based on the ItMLiHSmar2022 course

require(Hmisc)
require(dplyr)
# library(daDoctoR)
require(tidyverse)
require(patchwork)
require(caret)
require(glmnet)
require(leaps)
require(pROC)
require(gt)
require(gtsummary)
require(glue)
# library(ggdendro)
require(corrplot)
require(stRoke)

## ====================================================================
# Step 1: Import
## ====================================================================

if ("try-error" %in% class(t <- try(read.csv("/Volumes/Data/exercise/source/background.csv")))) {
  export <-
    read.csv(
      "/Volumes/Data 1/exercise/source/background.csv",
      colClasses = "character",
      na.strings = c("NA", "", "unknown")
    )
} else if (!"try-error" %in% class(t)) {
  export <-
    read.csv(
      "/Volumes/Data/exercise/source/background.csv",
      colClasses = "character",
      na.strings = c("NA", "", "unknown")
    )
}

## ====================================================================
# Step 2: Selection
## ====================================================================


export<-export[,c("pase_0",
               "age",
               "sex",
               "civil",
               "smoke_ever", 
               "smoker",
               "rtreat",
               "alc",
               "afli",
               "hypertension",
               "diabetes",
               "mrs_0",
               "nihss_c",
               "thrombolysis",
               "pad", 
               "thrombechtomy", 
               "ami", 
               "tci", 
               "pase_6")]

## ====================================================================
# Step 3: Formatting variables
## ====================================================================

export$diabetes[is.na(export$diabetes)]<-"no"
export$diabetes[is.na(export$hypertension)]<-"no"
export$thrombolysis[is.na(export$thrombolysis)]<-"no"
export$thrombechtomy[is.na(export$thrombechtomy)]<-"no"
export$pad[is.na(export$pad)]<-"no"
export$ami[is.na(export$ami)]<-"no"
# export$smoker_prev <- ifelse(export$smoker=="3","yes","no")
export$smoker <- ifelse(export$smoker=="1","yes","no")
export$smoker[is.na(export$smoker)] <- "no"
# export$mrs_0[export$mrs_0==3]<-NA

dta <- export %>% 
  # as_tibble()%>%
  mutate(any_rep=factor(ifelse(thrombolysis=="yes"|thrombechtomy=="yes","yes","no")), # If not noted, no therapy was received
         male_sex= factor(ifelse(sex=="female","no","yes")),
         # smoke_ever=factor(ifelse(smoke_ever=="never","no","yes")),
         civil=factor(ifelse(civil=="partner","no","yes")), # Sets "yes" for not-cohabiting
         rtreat=factor(ifelse(rtreat=="Placebo","no","yes")), # "Yes" receives active treatment
         alc=factor(ifelse(alc=="more","yes","no")), # Yes for more than guideline
         pase_0=as.numeric(pase_0),
         pase_6=as.numeric(pase_6),
         across(c("diabetes",
                  "hypertension",
                  "smoker",
                  "afli",
                  "pad",
                  "ami",
                  "tci",
                  "mrs_0"),as.factor),
         across(c("nihss_c",
                  "age"),as.numeric )
  )%>% 
  select(-c(sex))


## ====================================================================
# Step 4: Defining outcome
## ====================================================================

## Changed to step 7
## This is to perform proper quantile split based on actually included.

## ====================================================================
# Step 5: Ordering variables
## ====================================================================

vars <- c("age",
          "male_sex",
          "civil",
          "pase_0",
          "smoker",
          "alc",
          "afli",
          "hypertension",
          "diabetes",
          "pad",
          "ami",
          "tci",
          "mrs_0",
          "nihss_c",
          "any_rep",
          "rtreat",
          "pase_6")

dta<-dta[vars]

## ====================================================================
# Step 6: Labeling
## ====================================================================

var.labels = c(age="Age",
               male_sex="Male",
               civil="Living alone",
               pase_0="Pre-stroke PASE score",
               pase_6="Six month PASE score",
               smoker="Daily or occasinally smoking",
               alc="More alcohol than recommendation",
               afli="AFIB",
               hypertension="Hypertension",
               diabetes="Diabetes",
               pad="PAD",
               ami="Previous MI",
               tci="Previous TIA",
               mrs_0="Pre-stroke mRS  [-1]",
               nihss_c="Acute NIHSS score",
               thrombolysis="Acute thrombolysis",
               thrombechtomy="Acute thrombechtomy",
               any_rep="Any reperfusion therapy",
               rtreat="Active trial treatment",
               pase_drop_fac="PASE first quartile drop F",
               pase_hop_fac="PASE first quartile hop F",
               pase_0_cut="PASE 0 quartiles",
               pase_6_cut="PASE 6 quartiles")



## ====================================================================
# Step 7: final data export
## ====================================================================

data_summary<-summary(dta)

# Saving "old" factorised variables
sel<-sapply(dta,is.factor)
# Reformatting factors as 1/2 for analysis
dta<-dta |> 
  mutate(across(where(is.factor), as.numeric))|> # Turning factors into 1(no) or 2(yes) for model. Numbered alphabetically.
  mutate(across(matches(colnames(dta)[sel]), as.factor),
         across(starts_with("pase_"), as.numeric))

# Filtering out non-PASE
X_tbl<-dta |>
  filter(!is.na(pase_0),!is.na(pase_6))

nrow(X_tbl)

# Defining possible outcome meassures. Keeping in df for characterisation
X_tbl <- X_tbl|>
  mutate(## Relative decline
    pase_diff=(pase_0-pase_6),
    pase_decl_rel = pase_diff/pase_0*100,
    # pase_decl_rel_fac=factor(ifelse(pase_decl_rel>=rel_dif,"yes","no")),
    ## Absolute decline
    # pase_decl_abs_fac=factor(ifelse(pase_diff>=abs_dif,"yes","no")),
    ## Drop
    pase_0_cut=quantile_cut(as.numeric(pase_0),
                            groups=4,
                            group.names = c(as.character(1:4)),
                            y=as.numeric(pase_0),
                            ordered.f = TRUE,
                            inc.outs = TRUE#, 
                            # detail.lst=FALSE
                            ),
    pase_6_cut=quantile_cut(as.numeric(pase_6),
                            groups=4,
                            group.names = c(as.character(1:4)),
                            y=as.numeric(pase_0),
                            ordered.f = TRUE,
                            inc.outs = TRUE#, 
                            # detail.lst=FALSE
                            ),
    pase_drop_fac=factor(ifelse(pase_6_cut==1&pase_0_cut!=1,"yes","no")),
    pase_hop_fac=factor(ifelse(pase_6_cut!=1&pase_0_cut==1,"yes","no")))

Hmisc::label(X_tbl) = as.list(var.labels[match(names(X_tbl), names(var.labels))])