---
title: "Summarise concept id counts"
output: 
  html_document:
    pandoc_args: [
      "--number-offset=1,0"
      ]
    number_sections: yes
    toc: yes
vignette: >
  %\VignetteIndexEntry{summarise_concept_id_counts}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

```

# Introduction

In this vignette, we will explore the *OmopSketch* functions designed to provide information about the number of counts of concepts in tables. Specifically, there are two key functions that facilitate this, `summariseConceptIdCounts()` and `tableConceptIdCounts()`. The former one creates a summary statistics results with the number of counts per each concept in the clinical table, and the latter one displays the result in a table.

## Create a mock cdm

Let's see an example of the previous functions. To start with, we will load essential packages and create a mock cdm using `mockOmopSketch()`.

```{r, warning=FALSE}
library(duckdb)
library(OmopSketch)
library(dplyr)


cdm <- mockOmopSketch()

cdm
```

# Summarise concept id counts

We now use the `summariseConceptIdCounts()` function from the OmopSketch package to retrieve counts for each concept id and name, as well as for each source concept id and name, across the clinical tables.

```{r, warning=FALSE}
summariseConceptIdCounts(cdm, omopTableName = "drug_exposure") |>
  select(group_level, variable_name, variable_level, estimate_name, estimate_value, additional_name, additional_level) |>
  glimpse()
```

By default, the function returns the number of records (`estimate_name == "count_records"`) for each concept_id. To include counts by person, you can set the `countBy` argument to `"person"` or to c`("record", "person")` to obtain both record and person counts.

```{r, warning=FALSE}
summariseConceptIdCounts(cdm,
  omopTableName = "drug_exposure",
  countBy = c("record", "person")
) |>
  select( variable_name, estimate_name, estimate_value) 
```

Further stratification can be applied using the `interval`, `sex`, and `ageGroup` arguments. The interval argument supports "overall" (no time stratification), "years", "quarters", or "months".

```{r, warning=FALSE}
summariseConceptIdCounts(cdm,
  omopTableName = "condition_occurrence",
  countBy = "person",
  interval = "years",
  sex = TRUE,
  ageGroup = list("<=50" = c(0, 50), ">50" = c(51, Inf))
) |>
  select(group_level, strata_level, variable_name, estimate_name, additional_level) |>
  glimpse()
```

We can also filter the clinical table to a specific time window by setting the dateRange argument.

```{r}
summarisedResult <- summariseConceptIdCounts(cdm,
                                             omopTableName = "condition_occurrence",
                                             dateRange = as.Date(c("1990-01-01", "2010-01-01"))) 
summarisedResult |>
  omopgenerics::settings()|>
  glimpse()
```

Finally, you can summarise concept counts on a subset of records by specifying the `sample` argument.

```{r}
summariseConceptIdCounts(cdm,
                         omopTableName = "condition_occurrence",
                         sample = 50) |>
  select(group_level, variable_name, estimate_name) |>
  glimpse()

```

## Display the results

Finally, concept counts can be visualised using `tableConceptIdCounts()`. By default, it generates an interactive [reactable](https://glin.github.io/reactable/) table, but [DT](https://rstudio.github.io/DT/) datatables are also supported.

```{r, warning=FALSE}
result <- summariseConceptIdCounts(cdm,
  omopTableName = "measurement",
  countBy = "record"
) 
tableConceptIdCounts(result, type = "reactable")
```

```{r}
tableConceptIdCounts(result, type = "datatable")
```

The `display` argument in tableConceptIdCounts() controls which concept counts are shown. Available options include `display = "overall"`. It is the default option and it shows both standard and source concept counts.

```{r}
tableConceptIdCounts(result, display = "overall")

```

If `display = "standard"` the table shows only **standard** concept_id and concept_name counts.

```{r}
tableConceptIdCounts(result, display = "standard")

```

If `display = "source"` the table shows only **source** concept_id and concept_name counts.

```{r}
tableConceptIdCounts(result, display = "source")

```

If `display = "missing source"` the table shows only counts for concept ids that are missing a corresponding source concept id.

```{r}
tableConceptIdCounts(result, display = "missing source")

```

If `display = "missing standard"` the table shows only counts for source concept ids that are missing a mapped standard concept id.

```{r}
tableConceptIdCounts(result, display = "missing standard")

```

## Display the most frequent concepts

You can use the `tableTopConceptCounts()` function to display the most frequent concepts in a OMOP CDM table in formatted table. By default, the function returns a [gt](https://gt.rstudio.com/) table, but you can also choose from other output formats, including [flextable](https://davidgohel.github.io/flextable/), [datatable](https://rstudio.github.io/DT/), and [reactable](https://glin.github.io/reactable/).

```{r, warning=FALSE}
result <- summariseConceptIdCounts(cdm,
  omopTableName = "drug_exposure",
  countBy = "record"
) 
tableTopConceptCounts(result, type = "gt")
```

### Customising the number of top concepts

By default, the function shows the top 10 concepts. You can change this using the `top` argument:

```{r}
tableTopConceptCounts(result, top = 5)
```

### Choosing the count type

If your summary includes both record and person counts, you must specify which type to display using the `countBy` argument:

```{r, warning=FALSE}
result <- summariseConceptIdCounts(cdm,
  omopTableName = "drug_exposure",
  countBy = c("record", "person")
) 
tableTopConceptCounts(result, countBy = "person")
```
