Database Connection & Environment Setup

The NDQ package utilizes the argos system for interacting with data and accessing remote databases. To best utilize this tool, you will need to set up your environment to comply with the argos standards. Read more about the argos package and what is required for environment setup here.

Connecting to your CDM

The primary configuration in argos required to establish a database connection is config('db_src'). You can set this environment variable in one of two ways:

Option 1: DBI (or similar)

One option is to create a connection object inside your R session using DBI or a similar database connection package. Instructions for how to use DBI::dbConnect to establish a connection can be found in the DBI package documentation.

If this option is used, you can set your environment variable as config('db_src', myDBIobject)

Option 2: External JSON Configuration

Another option is to store your configuration details in a local JSON file and feed path to that file into srcr. We have provided a simple example below. As with Option 1, the type of information that would be included in the file differs for different database backends.

{
    "src_name" : "Postgres",
    "src_args" : {
            "host"     : "my.database.server",
            "port"     : 5432,
            "dbname"   : "project_db",
            "username" : "my_username",
            "password" : "my_password",
            "options"  : "-c search_path=my_cdm_schema"
     },
     "post_connect_sql" : [
         "set role project_staff;"
     ]
     
}

If this option is used, you can set your environment variable as config('db_src', srcr('path/to/my/file'))

Custom Configuration

The NDQ package also utilizes a custom environment variable, config('qry_site'), required only by this package and not argos as a whole. You should set it to the name of the institution for which you are executing the function(s), like config('qry_site', 'my_institution').

This configuration will ensure that, when applicable, only information from one institution is being read during the execution of each check_* function. This is done to improve performance and reduce the amount of data that is being processed at once. If you would like to execute these functions for multiple institutions, simply change the variable to another institution and re-execute the analysis. The process_* functions can be used to compute overall information based on the combined results of each execution.