This guide intends to server as an example of using RMM to build and maintain a package that produces results in an end to end manner. The aspects this package will cover are as follows:

  • Creating a basic package results specification
  • Using this specification to create a database schema and instantiating it in an SQLite database
  • Creating a database migration for this project

Setup R for data export

Project setup

First we will create an R package called SimpleFeatureExtractor a toy example that pulls a set of aggregate features for specified cohorts from an OMOP CDM and exports the result set to a relational database for further analysis.

In this example we will export a single csv file that contains the following:

table_name column_name data_type is_required primary_key optional empty_is_na
covariate_definition covariate_id int Yes Yes No No
covariate_definition covariate_name varchar Yes No No No
covariate_result cohort_definition_id int Yes Yes No No
covariate_result covariate_id bigint Yes Yes No No
covariate_result covariate_mean numeric Yes No No No

Results exported from this package are covariate prevalances related to a cohorts and given a covariate_id, these are related to names in a second table. This table should be saved to the inst folder of your R pacakge. Preferably, this file should be called resultsDataModelSpecification.csv

The package should create results csv files that correspond to these fields in terms of type and name.

Creating a results database schema

First we should load our specification

specification <- ResultModelManager::loadResultsDataModelSpecifications("resultsDataModelSpecification")

We can then create our schema from this sql:

sql <- ResultModelManager::generateSqlSchema(schemaDefinition = specification)

Viewing the sql we can see that we should add a database_schema parameter when executing the sql and table_prefix if we need it.

## {DEFAULT @table_prefix = ''}
## {DEFAULT @covariate_definition = covariate_definition}
## {DEFAULT @covariate_result = covariate_result}
## CREATE TABLE @database_schema.@table_prefix@covariate_definition (
##       covariate_id INT NOT NULL,
##   covariate_name VARCHAR,
##  PRIMARY KEY(covariate_id)
## );
## CREATE TABLE @database_schema.@table_prefix@covariate_result (
##       cohort_definition_id INT NOT NULL,
##   covariate_id BIGINT NOT NULL,
##   covariate_mean NUMERIC,
##  PRIMARY KEY(cohort_definition_id,covariate_id)
## );

We can then easily use this to create a schema using a QueryNamespace:

connectionDetails <- DatabaseConnector::createConnectionDetails(
  dbms = "sqlite",
  server = tempfile()
qns <- ResultModelManager::createQueryNamespace(
  connectionDetails = connectionDetails,
  tableSpecification = specification,
  tablePrefix = "my_study_",
  database_schema = "main"
## Connecting using SQLite driver
# note - the table prefix and schema parameters are not neeeded when we do this
##   |                                                                              |                                                                      |   0%  |                                                                              |===================================                                   |  50%  |                                                                              |======================================================================| 100%
## Executing SQL took 0.00729 secs

Alternatively, we can just use DatabaseConnector functions directly.

connection <- DatabaseConnector::connect(connectionDetails)
  table_prefix = "my_study_",
  database_schema = "main"

Uploading results

Now we have a schema we can upload results to it using the functionality exposed in this package. Using the above example, a results folder should have the following files:


File Name Description
covariate_definition.csv Covariate Definition File
covariate_result.csv Covariate Result File

We can now use the results spec to upload these files (and validate that they conform to the specification):

  schema = "main",
  resultsFolder = "results",
  tablePrefix = "my_study_",
  specifications = specification

With the results uploaded we can now write queries inside the namespace:

qns$queryDb("SELECT * FROM @database_schema.@covariate_definition")
## [1] covariateId   covariateName
## <0 rows> (or 0-length row.names)
qns$queryDb("SELECT * FROM @database_schema.@covariate_result WHERE cohort_definition_id = @cohort_id",
  cohort_id = 5
## [1] cohortDefinitionId covariateId        covariateMean     
## <0 rows> (or 0-length row.names)