---
title: "Database diagnostics"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{a01_DatabaseDiagnostics}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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


```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>", message = FALSE, warning = FALSE,
  fig.width = 7
)

library(CDMConnector)
if (Sys.getenv("EUNOMIA_DATA_FOLDER") == "") Sys.setenv("EUNOMIA_DATA_FOLDER" = tempdir())
if (!dir.exists(Sys.getenv("EUNOMIA_DATA_FOLDER"))) dir.create(Sys.getenv("EUNOMIA_DATA_FOLDER"))
if (!eunomiaIsAvailable()) downloadEunomiaData(datasetName = "synpuf-1k")
```

## Introduction
In this example we're going to be using the Eunomia synthetic data. 

```{r}
library(CDMConnector)
library(CohortConstructor)
library(CodelistGenerator)
library(PhenotypeR)
library(dplyr)
library(ggplot2)

con <- DBI::dbConnect(duckdb::duckdb(), 
                      CDMConnector::eunomiaDir("synpuf-1k", "5.3"))
cdm <- CDMConnector::cdmFromCon(con = con, 
                                cdmName = "Eunomia Synpuf",
                                cdmSchema   = "main",
                                writeSchema = "main", 
                                achillesSchema = "main")
```

## Database diagnostics

We have created our study cohort, but to inform analytic decisions and interpretation of results requires an understanding of the dataset from which it has been derived. The `databaseDiagnostics()` function will help us better understand a data source. 

To run database diagnostics we just need to provide our cdm reference to the function.

```{r}
db_diagnostics <- databaseDiagnostics(cdm)
db_diagnostics |> glimpse()
```

From our results we can create a table with a summary of metadata for the data source.
```{r}
OmopSketch::tableOmopSnapshot(db_diagnostics)
```

In addition, we also can see a summary of individuals' observation periods. From this we can see if there are individuals with multiple, non-overlapping, observation periods and how long each observation period lasts on average. 
```{r}
OmopSketch::tableObservationPeriod(db_diagnostics)
```
