example_report.Rmd

---
title: "Oxygen study monitoring report"
author: ""
date: "7/8/2021"
output: 
  html_document:
    toc: TRUE
    toc_depth: 4
    toc_float: TRUE
    toc_collapse: FALSE
    number_sections: TRUE
    highlight: pygments
    theme: spacelab
    code_folding: hide
    <!-- df_print: paged -->
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# Preparation and cleaning {.tabset}  

## Load packages

```{r}
source(here::here("scripts", "load_packages.R"))
```

## Pull RedCap data  

```{r}
source(here::here("scripts", "pull_redcap.R"))
o2_raw <- result
```

## Clean RedCap data  

```{r}
o2 <- o2_raw %>% 
     mutate(
          date_visit = ymd(date_visit),
          age_group = epikit::age_categories(
               age,
               breakers = seq(from = 0, to = 100, by = 5))
          )
     

# 
# select(
#           screen_id,
#           redcap_repeat_instance,
#           enrolled,
#           dob,
#           age,
#           calcage,
#           sex,
#           height,
#           weight,
#           bmi,
#           pregnant,
#           
#           date_visit,
#           time_visit,
#           referral,
#           
#      )

```

# Global overview {.tabset}

Summary table by study site: 

* Earliest data submission  
* Latest data submission  
* Number of patients considered  
* Number of patients enrolled  
* Percent of all enrolled patients  
* Daily patient data (% of expected)  


```{r, eval=F}
o2 %>% 
        group_b
     count(screen_id) %>% 
     tabyl(n) %>% 
     janitor::adorn_pct_formatting() %>% 
     rename(
          site = n,
          n_records = n_n,
     )

```

```{r, eval=F}
o2 %>%
     group_by(n) %>%   # site
     summarise(
          min_date = min(date_visit, na.rm=T),
          max_date = max(date_visit, na.rm=T)
     )
     

```


# Oxygen  

Descriptive analysis of the types of oxygen being provided  



# Survival {.tabset}

## Crude rates  

## Kaplan-Meier 

## Cause of death  


# Data quality {.tabset}

## Anticipated errors

## Outside bounds

## Inconsistencies  



# Enrolled Patients {.tabset}

## Transfers  

## Demographics  

### Age-sex  

```{r}
apyramid::age_pyramid(
     data = o2,
     age_group = age_group,
     split_by = sex,
     proportional = TRUE
)
```
### Pregnant  

```{r}
o2 %>% 
     tabyl(pregnant) %>% 
     adorn_pct_formatting()

```

## Records per patient  

### Total  

### Temporal distribution

## Duration of stay  

## Previous medical history (PMH)

```{r}
comor <- o2 %>% 
     select(screen_id,
            starts_with("pmh_")) %>% 
     mutate(across(
               .cols = starts_with("pmh_"),
               .fns = as.character),
            across(
               .cols = starts_with("pmh_"),
               .fns = fct_explicit_na)) %>% 
     pivot_longer(
          cols = starts_with("pmh_"),
          names_to = "comorbidity",
          values_to = "Value"
     )

ggplot(data = comor)+
     geom_bar(mapping = aes(x = comorbidity, group = Value, fill = Value))+
     coord_flip()+
     theme_minimal()

```

## Withdrawal  

```{r}

```



# Site summaries {.tabset}

Summary information per site, with links to full reports per site.

## Site 1  

## Site 2  

## Site 3  

## Site 4  

## Site 5  

## Site 6  

## Site 7