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

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

```{r setup, messages = FALSE, include = FALSE}
library(LTASR)
library(dplyr)
library(tidyr)
library(ggplot2)
library(readr)
library(purrr)
library(stringr)
library(knitr)
```
When stratifying a cohort, it is generally desirable to calculate SMRs for different levels of a strata (such as a time-dependent exposure).  

LTASR provides options to stratify a cohort by a fixed strata defined within the person file, or by a time-dependent exposure variable with information found in a separate history file.  

For example, below will strata the example person and history file, included in LTASR, by a cumulative exposure variable `exposure_level`:  
```{r, message=FALSE, results='hide'}
#Define exposure cutpoints
exp <- exp_strata(var = 'exposure_level',
                   cutpt = c(-Inf, 0, 10000, 20000, Inf),
                   lag = 10)

#Read in and format person file
person <- person_example %>%
  mutate(dob = as.Date(dob, format='%m/%d/%Y'),
         pybegin = as.Date(pybegin, format='%m/%d/%Y'),
         dlo = as.Date(dlo, format='%m/%d/%Y'))

#Read in and format history file
history <- history_example %>%
  mutate(begin_dt = as.Date(begin_dt, format='%m/%d/%Y'),
         end_dt = as.Date(end_dt, format='%m/%d/%Y')) 

#Stratify cohort
py_table <- get_table_history(persondf = person,
                              rateobj = us_119ucod_recent,
                              historydf = history,
                              exps = list(exp))
```  
  
This creates the following table (top 6 rows):  
```{r, echo=FALSE}
py_table %>%
  head() %>%
  kable()
```  
  
`smr_minor` and `smr_major` will calculate SMRs for the entire cohort that is read in.  

To calculate SMRs separately for each strata of `exposure_levelCat`, one option would be to create separate person-year tables for each level:  
```{r}
#Subset py_table to the highest exposed group
py_table_high <- py_table %>%
  filter(exposure_levelCat == '(2e+04, Inf]')

smr_minor_table_high <- smr_minor(py_table_high, us_119ucod_recent)
smr_major_table_high <- smr_major(smr_minor_table_high, us_119ucod_recent)
```  
```{r, echo=FALSE}
smr_minor_table_high %>%
  filter(minor %in% c(55, 52)) %>%
  head() %>%
  kable(digits = 2)
smr_major_table_high %>%
  filter(major %in% c(16)) %>%
  head() %>%
  kable(digits = 2)
```  
These results can be saved through repeated calls to `write_csv()`. This can be tedious for strata with many levels.  

Alternatively, the below code will loop through each level of the a variable (defined by `var`) and outputs results into an excel file (using the `writexl` library) with a separate tab for each strata level:  
```{r, eval=FALSE}
#Define the name of the person year table (py_table)
#and the variable to calcualte SMRs accross
pyt <- py_table
var <- 'exposure_levelCat'

#Loop through levels of the above variable
lvls <- unique(pyt[var][[1]])
smr_minors <- 
  map(lvls,
    ~ {
      pyt %>%
        filter(!!sym(var) == .x) %>%
        smr_minor(us_119ucod_recent)
    }) %>%
  setNames(lvls)

smr_majors <- 
  map(smr_minors,
      ~ smr_major(., us_119ucod_recent))%>%
  setNames(names(smr_minors))

#Adjust names of sheets
names(smr_minors) <- str_replace_all(names(smr_minors), "\\[|\\]", "_")
names(smr_majors) <- str_replace_all(names(smr_majors), "\\[|\\]", "_")

#Save results 
library(writexl)
write_xlsx(smr_minors, 'C:/SMR_Minors_by_exp.xlsx')
write_xlsx(smr_majors, 'C:/SMR_Majors_by_exp.xlsx')
```
