---
title: "Example Wrapped Approach"
output: 
  rmarkdown::html_vignette
    
vignette: >
  %\VignetteIndexEntry{Example Wrapped Approach}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

# Introduction

This article is an example workflow of the wrapped approach where modules are wrapped into a cut_data function.

# Example



```{r, message=FALSE, warning=FALSE}
# Name: Datacut Template Code - Modular Approach

# Creating data to be cut ------------------------------------------------
library(datacutr)
library(admiraldev)
library(dplyr)
library(lubridate)
library(stringr)
library(purrr)

# Name: Datacut Template Code - Wrapped Approach

# Creating data to be cut ------------------------------------------------

source_data <- list(
  ds = datacutr_ds, dm = datacutr_dm, ae = datacutr_ae, sc = datacutr_sc,
  lb = datacutr_lb, fa = datacutr_fa, ts = datacutr_ts
)

# Create DCUT ------------------------------------------------------------

dcut <- create_dcut(
  dataset_ds = source_data$ds,
  ds_date_var = DSSTDTC,
  filter = DSDECOD == "RANDOMIZATION",
  cut_date = "2022-06-04",
  cut_description = "Clinical Cutoff Date"
)


# Pre-processing of FA ----------------------------------------------------

# Update FA
source_data$fa <- source_data$fa %>%
  mutate(DCUT_TEMP_FAXDTC = case_when(
    FASTDTC != "" ~ FASTDTC,
    FADTC != "" ~ FADTC,
    TRUE ~ as.character(NA)
  ))


# Process data cut --------------------------------------------------------

cut_data <- process_cut(
  source_sdtm_data = source_data,
  patient_cut_v = c("sc", "ds"),
  date_cut_m = rbind(
    c("ae", "AESTDTC"),
    c("lb", "LBDTC"),
    c("fa", "DCUT_TEMP_FAXDTC")
  ),
  no_cut_v = c("ts"),
  dataset_cut = dcut,
  cut_var = DCUTDTM,
  special_dm = TRUE
)
```
