---
title: "Filtering cohorts"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{a07_filter_cohorts}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

```{r setup}
library(CohortConstructor)
library(CohortCharacteristics)
library(ggplot2)
```

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

library(CDMConnector)
library(dplyr, warn.conflicts = FALSE)

if (Sys.getenv("EUNOMIA_DATA_FOLDER") == ""){
  Sys.setenv("EUNOMIA_DATA_FOLDER" = file.path(tempdir(), "eunomia"))}
if (!dir.exists(Sys.getenv("EUNOMIA_DATA_FOLDER"))){ dir.create(Sys.getenv("EUNOMIA_DATA_FOLDER"))
  downloadEunomiaData()  
}
```

For this example we'll use the Eunomia synthetic data from the CDMConnector package.

```{r}
con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomia_dir())
cdm <- cdm_from_con(con, cdm_schema = "main", 
                    write_schema = c(prefix = "my_study_", schema = "main"))
```

Let's start by creating two drug cohorts, one for users of diclofenac and another for users of acetaminophen.

```{r}
cdm$medications <- conceptCohort(cdm = cdm, 
                                 conceptSet = list("diclofenac" = 1124300,
                                                   "acetaminophen" = 1127433), 
                                 name = "medications")
cohortCount(cdm$medications)
```
We can take a sample from a cohort table using the function `sampleCohort()`. This allows us to specify the number of individuals in each cohort. 

```{r}
cdm$medications |> sampleCohorts(cohortId = NULL, n = 100)

cohortCount(cdm$medications)
```
When cohortId = NULL all cohorts in the table are used. Note that this function does not reduced the number of records in each cohort, only the number of individuals.

It is also possible to only sample one cohort within cohort table, however the remaining cohorts will still remain.

```{r include = FALSE, warning = FALSE}
con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomia_dir())
cdm <- cdm_from_con(con, cdm_schema = "main", 
                    write_schema = c(prefix = "my_study_", schema = "main"))
cdm$medications <- conceptCohort(cdm = cdm, 
                                 conceptSet = list("diclofenac" = 1124300,
                                                   "acetaminophen" = 1127433), 
                                 name = "medications")
```

```{r}
cdm$medications <- cdm$medications |> sampleCohorts(cohortId = 2, n = 100)

cohortCount(cdm$medications)
```

The chosen cohort (users of diclofenac) has been reduced to 100 individuals, as specified in the function, however all individuals from cohort 1 (users of acetaminophen) and their records remain.

If you want to filter the cohort table to only include individuals and records from a specified cohort, you can use the function `subsetCohorts`. 

```{r include = FALSE, warning = FALSE}
con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomia_dir())
cdm <- cdm_from_con(con, cdm_schema = "main", 
                    write_schema = c(prefix = "my_study_", schema = "main"))
cdm$medications <- conceptCohort(cdm = cdm, 
                                 conceptSet = list("diclofenac" = 1124300,
                                                   "acetaminophen" = 1127433), 
                                 name = "medications")
```

```{r}
cdm$medications <- cdm$medications |> subsetCohorts(cohortId = 2)
cohortCount(cdm$medications)
```
The cohort table has been filtered so it now only includes individuals and records from cohort 2. If you want to take a sample of the filtered cohort table then you can use the `sampleCohorts` function.

```{r}
cdm$medications <- cdm$medications |> sampleCohorts(cohortId = 2, n = 100)

cohortCount(cdm$medications)
```
