---
title: "Types of Codelist"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{a03_TypesOfCodelist}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
NOT_CRAN <- identical(tolower(Sys.getenv("NOT_CRAN")), "true")
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = NOT_CRAN
)
```

In this vignette we will introduce the three different types of codelist that CodelistGenerator uses: codelist, codelist with details, and concept set. First of all, we will upload the packages required for the demonstration.

```{r}
library(omopgenerics, warn.conflicts = FALSE)
library(dplyr, warn.conflicts = FALSE)
library(CodelistGenerator)
```

## Codelist

Codelist format is the simplest of the three formats. To create one, we just need a named list where each element contains a vector of concept IDs. You can see an example below on how to create one:

```{r}
codelist <- list("codes1" = c(1L, 2L, 3L),
                 "codes2" = c(4L, 5L, 10L))
codelist <- newCodelist(codelist)

codelist 
```

Notice that the codelist has two specific attributes, the name of the codelists that contains and the class:
```{r}
attributes(codelist)
```

### Create an empty codelist 

We can create an empty codelist using omopgenerics package:

```{r}
empty_codelist  <- emptyCodelist()

empty_codelist
```

## Codelist with details

Codelist with details contains a tibble in each element. To create one, we need a named list where each element contains a tibble with at least the column *concept_id*.

```{r}
codelist_with_details <- list("codes1" = tibble("concept_id" = c(1L, 2L, 3L)),
                              "codes2" = tibble("concept_id" = c(4L, 5L, 10L)))
codelist_with_details <- newCodelistWithDetails(codelist_with_details)

codelist_with_details
```

Notice that this allows us to add more information of each one of the codes:

```{r}
codelist_with_details <- list(
  "codes1" = tibble("concept_id" = c(1L, 2L, 3L),
                    "concept_name" = c("Musculoskeletal disorder", "Osteoarthrosis", "Arthritis"),
                    "domain_id" = c("Condition", "Condition", "Condition"),
                    "vocabulary_id" = c("SNOMED","SNOMED","SNOMED"),
                    "standard_concept" = c("S","S","S")),
  "codes2" = tibble("concept_id" = c(4L, 5L, 10L),
                    "concept_name" = c("Osteoarthritis of knee", "Osteoarthritis of hip", "Adalimumab"),
                    "domain_id" = c("Condition", "Condition", "Drug"),
                    "vocabulary_id" = c("SNOMED","SNOMED","RxNorm"),
                    "standard_concept" = c("S","S","S")))
codelist_with_details <- newCodelistWithDetails(codelist_with_details)

codelist_with_details
```

The codelist with details also has two specific attributes:
```{r}
attributes(codelist_with_details)
```

### Create an empty codelist with details

omopgenerics package also has a function to create an empty codelist with details.

```{r}
empty_codelist_with_details <- emptyCodelistWithDetails()

empty_codelist_with_details
```

## Concept set expression

A concept set expression contains a tibble with the logic describing how this concepts will be included or excluded. It usually follows the JSON file structure. To create one, we need a named list with a tibble containing the columns: *concept_id*, *excluded*, *descendants*, and *mapped*:

```{r}
concept_set_expression <- list(
  "codes1" = tibble("concept_id" = c(1L, 2L, 3L),
                    "excluded" = c(FALSE, FALSE, FALSE),
                    "descendants" = c(TRUE, FALSE, FALSE),
                    "mapped" = c(TRUE, TRUE, TRUE)),
  "codes2" = tibble("concept_id" = c(4L, 5L, 10L),
                    "excluded" = c(FALSE, FALSE, FALSE),
                    "descendants" = c(FALSE, FALSE, FALSE),
                    "mapped" = c(TRUE, TRUE, TRUE))
)

concept_set_expression <- newConceptSetExpression(concept_set_expression)

concept_set_expression
```

You can also see the attributes:
```{r}
attributes(concept_set_expression)
```

### Create an empty concept set expression

We can also use omopgenerics package to create an empty concept set expression:

```{r}
empty_concept_set_expression <- emptyConceptSetExpression()

empty_concept_set_expression
```

### Concept set expression from a JSON file

CodelistGenerator contains a function that allows us to read a JSON file and transform it to a concept set expression (or other types of codelists). See the vignette **Extract codelists from JSON files** for further information.

## Change to different codelist types

There are several functions in CodelistGenerator that will allow us to go from a codelist to a codelist with details.

### Codelist/Concept set expression to codelist with details

Notice that to go from a codelist or concept_set_expression to a codelist_with_details, we will need to provide the cdm, as the details of the concepts will be extracted from the *concept* table.

```{r}
cdm <- mockVocabRef()
codelist_to_codelist_with_details <- asCodelistWithDetails(codelist, cdm)
codelist_to_codelist_with_details

codelist_to_concept_set_expression <- asCodelistWithDetails(codelist, cdm)
codelist_to_concept_set_expression

```

### Codelist/Codelist with details to concept set expression

```{r}

codelist_to_concept_set_expression <- asConceptSetExpression(codelist)
codelist_to_concept_set_expression

codelist_with_details_to_concept_set_expression <- asConceptSetExpression(codelist_with_details)
codelist_with_details_to_concept_set_expression
```
### Codelist with details/Concept set expression to codelist
```{r}
codelist_with_details_to_codelist <- asCodelist(codelist_with_details)
codelist_with_details_to_codelist

```
