update docs for v2.0.4

OHDSI · Jul 3, 2023 · cb9cf4b · cb9cf4b
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diff --git a/NEWS.md b/NEWS.md
@@ -2,8 +2,7 @@ Capr 2.0.4
 ==========
 - change query functions to match known syntax (i.e. drug => drugExposure, condition => conditionOccurrence)
 - require a name for `cs()`
-- improve documentation
-    - add vignette for query, count and group
+- improve documentation (add vignette for query, count and group)
 
 Capr 2.0.3
 ==========

diff --git a/README.md b/README.md
@@ -16,11 +16,6 @@ Learn more about the OHDSI approach to cohort building in the [cohorts chapter o
 
 # Installation
 
-Capr can be installed via:
-
-``` r
-# install.packages("Capr")
-```
 
 Users can install the current development version of Capr from [GitHub](https://github.com/) with:
 
@@ -29,242 +24,14 @@ Users can install the current development version of Capr from [GitHub](https://
 devtools::install_github("ohdsi/Capr")
 ```
 
-# How to Use
-
-## Examples
-
-Capr uses many defaults that match the defaults in Atlas. Creating a simple cohort is a single line of code. As an example we will define a cohort of new users of diclofenac described in the [Book of OHDSI.](https://ohdsi.github.io/TheBookOfOhdsi/SuggestedAnswers.html#Cohortsanswers)
-
-### Simple diclofenac cohort
-
-``` r
-library(Capr)
-
-# Define concepts sets with cs()
-diclofenac <- cs(descendants(1124300))
-
-ch <- cohort(
-  entry = entry(drugEra(diclofenac))
-)
-
-ch
-#> Formal class 'Cohort' [package "Capr"] with 4 slots
-#>   ..@ entry    :Formal class 'CohortEntry' [package "Capr"] with 5 slots
-#>   ..@ attrition:Formal class 'CohortAttrition' [package "Capr"] with 2 slots
-#>   ..@ exit     :Formal class 'CohortExit' [package "Capr"] with 2 slots
-#>   ..@ era      :Formal class 'CohortEra' [package "Capr"] with 3 slots
-```
-
-### Adding more complexity
-
-We can make more complex cohorts by adding a window of continuous observation and a custom cohort exit. The following information was added to the diclofenac cohort:
-
--   Ages 16 or older
--   With at least 365 days of continuous observation prior to exposure
--   With cohort exit defined as discontinuation of exposure (allowing for a 30-day gap)
-
-``` r
-diclofenac <- cs(descendants(1124300))
-
-ch <- cohort(
-  entry = entry(drugEra(diclofenac, age(gte(16))),
-                observationWindow = continuousObservation(-365L, 0L)),
-  exit = exit(drugExit(diclofenac))
-)
-
-ch
-#> Formal class 'Cohort' [package "Capr"] with 4 slots
-#>   ..@ entry    :Formal class 'CohortEntry' [package "Capr"] with 5 slots
-#>   ..@ attrition:Formal class 'CohortAttrition' [package "Capr"] with 2 slots
-#>   ..@ exit     :Formal class 'CohortExit' [package "Capr"] with 2 slots
-#>   ..@ era      :Formal class 'CohortEra' [package "Capr"] with 3 slots
-```
-
-### Adding cohort attrition
-
-Users can also add attrition to the cohort by specifying inclusion and exclusion criteria to modify the cohort entry. The following exclusion criteria were added to the diclofenac cohort:
-
--   Without prior exposure to any NSAID (Non-Steroidal Anti-Inflammatory Drug)
--   Without prior diagnosis of cancer
-
-``` r
-diclofenac <- cs(descendants(1124300), name = "diclofenac")
-nsaid <- cs(descendants(21603933), name = "nsaid")
-cancer <- cs(descendants(443392), name = "cancer")
-
-ch <- cohort(
-  entry = entry(drugEra(diclofenac, age(gte(16))),
-                observationWindow = continuousObservation(-365L, 0L)),
-  attrition = attrition(
-    withAll(
-      exactly(0, drug(nsaid), eventStarts(-Inf, 0, index = "startDate")),
-      exactly(0, condition(cancer), eventStarts(-Inf, 0, index = "startDate"))
-    )
-  ),
-  exit = exit(
-    endStrategy = drugExit(diclofenac, persistenceWindow = 30)
-    )
-)
-
-ch
-#> Formal class 'Cohort' [package "Capr"] with 4 slots
-#>   ..@ entry    :Formal class 'CohortEntry' [package "Capr"] with 5 slots
-#>   ..@ attrition:Formal class 'CohortAttrition' [package "Capr"] with 2 slots
-#>   ..@ exit     :Formal class 'CohortExit' [package "Capr"] with 2 slots
-#>   ..@ era      :Formal class 'CohortEra' [package "Capr"] with 3 slots
-```
-
-## Save cohort as JSON
-
-OHDSI standard cohorts are represented as json files and can be copy and pasted into Atlas.
-
-``` r
-
-path <- file.path(tempdir(), "diclofenacCohort.json")
-
-writeCohort(ch, path)
-
-cat(substr(readr::read_file(path), 1, 100))
-#> {
-#>   "ConceptSets": [
-#>     {
-#>       "id": 0,
-#>       "name": "diclofenac",
-#>       "expression": {
-#> 
-```
-
-### Fill in missing concept set details
-
-Users can build valid cohorts with minimal concept information, only supplying a concept id and name. The example below shows the minimal concept set input for Capr.
-
-``` r
-
-diclofenac <- cs(descendants(1124300), name = "diclofenac")
-
-cat(as.json(diclofenac))
-#> {
-#>   "id": "11d012608fce118593830a3039042e56",
-#>   "name": "diclofenac",
-#>   "expression": {
-#>     "items": [
-#>       {
-#>         "concept": {
-#>           "CONCEPT_ID": 1124300,
-#>           "CONCEPT_NAME": "",
-#>           "STANDARD_CONCEPT": "",
-#>           "STANDARD_CONCEPT_CAPTION": "",
-#>           "INVALID_REASON": "",
-#>           "INVALID_REASON_CAPTION": "",
-#>           "CONCEPT_CODE": "",
-#>           "DOMAIN_ID": "",
-#>           "VOCABULARY_ID": "",
-#>           "CONCEPT_CLASS_ID": ""
-#>         },
-#>         "isExcluded": false,
-#>         "includeDescendants": true,
-#>         "includeMapped": false
-#>       }
-#>     ]
-#>   }
-#> }
-```
-
-However, when saving cohorts it is helpful to fill in the concept details. This requires a live connection to an OMOP CDM database that includes the vocabularies used in the cohort definition.
-
-``` r
-con <- DatabaseConnector::connect(Eunomia::getEunomiaConnectionDetails())
-diclofenac <- getConceptSetDetails(diclofenac, con, vocabularyDatabaseSchema = "main")
-cat(as.json(diclofenac))
-#> {
-#>   "id": "11d012608fce118593830a3039042e56",
-#>   "name": "diclofenac",
-#>   "expression": {
-#>     "items": [
-#>       {
-#>         "concept": {
-#>           "CONCEPT_ID": 1124300,
-#>           "CONCEPT_NAME": "Diclofenac",
-#>           "STANDARD_CONCEPT": "S",
-#>           "STANDARD_CONCEPT_CAPTION": "Standard",
-#>           "INVALID_REASON": "V",
-#>           "INVALID_REASON_CAPTION": "Valid",
-#>           "CONCEPT_CODE": "3355",
-#>           "DOMAIN_ID": "Drug",
-#>           "VOCABULARY_ID": "RxNorm",
-#>           "CONCEPT_CLASS_ID": "Ingredient"
-#>         },
-#>         "isExcluded": false,
-#>         "includeDescendants": true,
-#>         "includeMapped": false
-#>       }
-#>     ]
-#>   }
-#> }
-```
-
-### Generating Capr Cohorts
-
-Once a Capr cohort has been constructed, the user can generate this cohort definition on an OMOP CDM connection. It is suggested to use [CohortGenerator](https://github.com/OHDSI/CohortGenerator) and [CirceR](https://github.com/OHDSI/CirceR) to generate Capr cohorts on a database.
-
-## Building Capr Templates
-
-A Capr cohort template is a function that always returns a Capr cohort. It can accept arguments that can be used to parameterize any part of a cohort definition. Capr cohort templates are the recommended approach for building large numbers of similar cohorts in R.
-
-``` r
-
-# A Capr cohort template is a function that returns a cohort
-drugEraTemplate <- function(ingredientConceptId) {
-
-  drugConceptSet <- cs(descendants(ingredientConceptId))
-
-  cohort(
-    entry = entry(drugEra(drugConceptSet, age(gte(16))),
-                  observationWindow = continuousObservation(-365L, 0L)),
-    exit = exit(drugExit(drugConceptSet, persistenceWindow = 30))
-  )
-}
-
-
-library(dplyr, warn.conflicts = FALSE)
-
-# create a cohort for every single ingredient
-df <- DBI::dbGetQuery(con, 
-                      "Select * from concept where concept_class_id = 'Ingredient'") %>% 
-  tibble() %>% 
-  select(concept_id, concept_name) %>% 
-  mutate(capr_cohort = purrr::map(concept_id, drugEraTemplate)) %>% 
-  mutate(cohort_json = purrr::map_chr(capr_cohort, as.json))
-
-df
-#> # A tibble: 91 × 4
-#>    concept_id concept_name    capr_cohort cohort_json                           
-#>         <dbl> <chr>           <list>      <chr>                                 
-#>  1    1557272 Alendronate     <Cohort>    "{\n  \"ConceptSets\": [\n    {\n    …
-#>  2     708298 Midazolam       <Cohort>    "{\n  \"ConceptSets\": [\n    {\n    …
-#>  3     701322 Memantine       <Cohort>    "{\n  \"ConceptSets\": [\n    {\n    …
-#>  4     723013 Diazepam        <Cohort>    "{\n  \"ConceptSets\": [\n    {\n    …
-#>  5    1129625 Diphenhydramine <Cohort>    "{\n  \"ConceptSets\": [\n    {\n    …
-#>  6    1149196 Cetirizine      <Cohort>    "{\n  \"ConceptSets\": [\n    {\n    …
-#>  7    1149380 fluticasone     <Cohort>    "{\n  \"ConceptSets\": [\n    {\n    …
-#>  8    1150770 Astemizole      <Cohort>    "{\n  \"ConceptSets\": [\n    {\n    …
-#>  9    1150836 Terfenadine     <Cohort>    "{\n  \"ConceptSets\": [\n    {\n    …
-#> 10    1124300 Diclofenac      <Cohort>    "{\n  \"ConceptSets\": [\n    {\n    …
-#> # … with 81 more rows
-```
-
-The capr_cohort column of the dataframe is a list of Capr cohort object. The cohort_json column contains the json specifications for each cohort.
-
-``` r
-DatabaseConnector::disconnect(con)
-```
 
 # User Documentation
 
 Documentation can be found on the [package website](https://ohdsi.github.io/Capr/).
 
 -   Vignette: [Using Capr](https://ohdsi.github.io/Capr/articles/Using-Capr.html)
 -   Vignette: [Examples](https://ohdsi.github.io/Capr/articles/Examples.html)
+-   Vignette: [Capr Components](https://ohdsi.github.io/Capr/articles/capr_objects.html)
 -   [Design Document](https://ohdsi.github.io/Capr/articles/capr_design.html)
 -   [Package manual](https://raw.githubusercontent.com/OHDSI/Capr/main/extras/Capr.pdf)
 

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