Title:	Connect to an OMOP Common Data Model
Version:	2.1.1
Description:	Provides tools for working with observational health data in the Observational Medical Outcomes Partnership (OMOP) Common Data Model format with a pipe friendly syntax. Common data model database table references are stored in a single compound object along with metadata.
License:	Apache License (≥ 2)
URL:	https://darwin-eu.github.io/CDMConnector/, https://github.com/darwin-eu/CDMConnector
BugReports:	https://github.com/darwin-eu/CDMConnector/issues
Encoding:	UTF-8
RoxygenNote:	7.3.2
Depends:	R (≥ 4.1.0)
Imports:	dplyr, dbplyr (≥ 2.5.0), DBI (≥ 0.3.0), checkmate, cli, purrr, rlang, tidyselect, glue, methods, withr, lifecycle, stringr, stringi, generics, tidyr, jsonlite, readr, omopgenerics (≥ 1.2.0)
Suggests:	SqlRender, CirceR, rJava, covr, knitr, rmarkdown, duckdb, RSQLite, RPostgres, DatabaseConnector, odbc, ggplot2, bigrquery, lubridate, clock, tibble, testthat (≥ 3.0.0), pool, snakecase, palmerpenguins
Enhances:	arrow
Config/testthat/edition:	3
Config/testthat/parallel:	false
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-07-10 12:17:16 UTC; ablack
Author:	Adam Black [aut, cre], Artem Gorbachev [aut], Edward Burn [aut], Marti Catala Sabate [aut], Ioanna Nika [aut]
Maintainer:	Adam Black <black@ohdsi.org>
Repository:	CRAN
Date/Publication:	2025-07-10 12:50:16 UTC

CDMConnector: Connect to an OMOP Common Data Model

Description

Provides tools for working with observational health data in the Observational Medical Outcomes Partnership (OMOP) Common Data Model format with a pipe friendly syntax. Common data model database table references are stored in a single compound object along with metadata.

Author(s)

Maintainer: Adam Black black@ohdsi.org (ORCID)

Authors:

• Artem Gorbachev artem.gorbachev@odysseusinc.com

• Edward Burn edward.burn@ndorms.ox.ac.uk

• Marti Catala Sabate marti.catalasabate@ndorms.ox.ac.uk

• Ioanna Nika i.nika@darwin-eu.org

See Also

Useful links:

• https://darwin-eu.github.io/CDMConnector/

• https://github.com/darwin-eu/CDMConnector

• Report bugs at https://github.com/darwin-eu/CDMConnector/issues

Pipe operator

Description

Pipe operator

Usage

lhs %>% rhs

Arguments

Value

The result of calling rhs(lhs).

Run a dplyr query and add the result set to an existing

Description

Run a dplyr query and add the result set to an existing

Usage

appendPermanent(x, name, schema = NULL)

Arguments

Value

A dplyr reference to the newly created table

Examples

## Not run: 
library(CDMConnector)

con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomiaDir())
concept <- dplyr::tbl(con, "concept")

# create a table
rxnorm_count <- concept %>%
  dplyr::filter(domain_id == "Drug") %>%
  dplyr::mutate(isRxnorm = (vocabulary_id == "RxNorm")) %>%
  dplyr::count(domain_id, isRxnorm) %>%
  compute("rxnorm_count")

# append to an existing table
rxnorm_count <- concept %>%
  dplyr::filter(domain_id == "Procedure") %>%
  dplyr::mutate(isRxnorm = (vocabulary_id == "RxNorm")) %>%
  dplyr::count(domain_id, isRxnorm) %>%
  appendPermanent("rxnorm_count")

DBI::dbDisconnect(con, shutdown = TRUE)


## End(Not run)

as.Date dbplyr translation wrapper

Description

This is a workaround for using as.Date inside dplyr verbs against a database backend. This function should only be used inside dplyr verbs where the first argument is a database table reference. asDate must be unquoted with !! inside dplyr verbs (see example).

Usage

asDate(x)

Arguments

Examples

## Not run: 
con <- DBI::dbConnect(odbc::odbc(), "Oracle")
date_tbl <- dplyr::copy_to(con,
                           data.frame(y = 2000L, m = 10L, d = 10L),
                           name = "tmp",
                           temporary = TRUE)

df <- date_tbl %>%
  dplyr::mutate(date_from_parts = !!asDate(paste0(
    .data$y, "/",
    .data$m, "/",
    .data$d
  ))) %>%
  dplyr::collect()

## End(Not run)

Run benchmark of tasks using CDMConnector

Description

Run benchmark of tasks using CDMConnector

Usage

benchmarkCDMConnector(cdm)

Arguments

Value

a tibble with time taken for different analyses

Examples

## Not run: 
library(CDMConnector)
con <- DBI::dbConnect(duckdb::duckdb(), eunomiaDir())
cdm <- cdmFromCon(con, cdmSchema = "main", writeSchema = "main")
benchmarkCDMConnector(cdm)

DBI::dbDisconnect(con, shutdown = TRUE)

## End(Not run)

Get underlying database connection

Description

Get underlying database connection

Usage

cdmCon(cdm)

Arguments

Value

A reference to the database containing tables in the cdm reference

Examples

## Not run: 
con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomiaDir())

cdm <- cdmFromCon(con = con, cdmName = "Eunomia",
                  cdmSchema =  "main", writeSchema = "main")

cdmCon(cdm)

DBI::dbDisconnect(con)

## End(Not run)

Disconnect the connection of the cdm object

Description

This function will disconnect from the database as well as drop "temporary" tables that were created on database systems that do not support actual temporary tables. Currently temp tables are emulated on Spark/Databricks systems.

Usage

## S3 method for class 'db_cdm'
cdmDisconnect(cdm, dropWriteSchema = FALSE, ...)

Arguments

Flatten a cdm into a single observation table

Description

This experimental function transforms the OMOP CDM into a single observation table. This is only recommended for use with a filtered CDM or a cdm that is small in size.

Usage

cdmFlatten(
  cdm,
  domain = c("condition_occurrence", "drug_exposure", "procedure_occurrence"),
  includeConceptName = TRUE
)

Arguments

Value

A lazy query that when evaluated will result in a single table

Examples

## Not run: 
library(CDMConnector)
library(dplyr, warn.conflicts = FALSE)

con <- DBI::dbConnect(duckdb::duckdb(), eunomiaDir())

cdm <- cdmFromCon(con, cdmSchema = "main")

all_observations <- cdmSubset(cdm, personId = c(2, 18, 42)) %>%
  cdmFlatten() %>%
  collect()

all_observations
#> # A tibble: 213 × 8
#>    person_id observation_.  start_date end_date   type_.  domain obser.  type_.
#>        <dbl>          <dbl> <date>     <date>       <dbl> <chr>  <chr>   <chr>
#>  1         2       40213201 1986-09-09 1986-09-09  5.81e5 drug   pneumo  <NA>
#>  2        18        4116491 1997-11-09 1998-01-09  3.20e4 condi  Escher  <NA>
#>  3        18       40213227 2017-01-04 2017-01-04  5.81e5 drug   tetanu  <NA>
#>  4        42        4156265 1974-06-13 1974-06-27  3.20e4 condi  Facial  <NA>
#>  5        18       40213160 1966-02-23 1966-02-23  5.81e5 drug   poliov  <NA>
#>  6        42        4198190 1933-10-29 1933-10-29  3.80e7 proce  Append  <NA>
#>  7         2        4109685 1952-07-13 1952-07-27  3.20e4 condi  Lacera  <NA>
#>  8        18       40213260 2017-01-04 2017-01-04  5.81e5 drug   zoster  <NA>
#>  9        42        4151422 1985-02-03 1985-02-03  3.80e7 proce  Sputum  <NA>
#> 10         2        4163872 1993-03-29 1993-03-29  3.80e7 proce  Plain   <NA>
#> # ... with 203 more rows, and abbreviated variable names observation_concept_id,
#> #   type_concept_id, observation_concept_name, type_concept_name

DBI::dbDisconnect(con, shutdown = TRUE)

## End(Not run)

Create a CDM reference object from a database connection

Description

Create a CDM reference object from a database connection

Usage

cdmFromCon(
  con,
  cdmSchema,
  writeSchema,
  cohortTables = NULL,
  cdmVersion = NULL,
  cdmName = NULL,
  achillesSchema = NULL,
  .softValidation = FALSE,
  writePrefix = NULL
)

Arguments

Details

cdmFromCon creates a new cdm reference object from a DBI database connection. In addition to the connection the user needs to pass in the schema in the database where the cdm data can be found as well as another schema where the user has write access to create tables. Nearly all downstream analytic packages need the ability to create temporary data in the database so the write_schema is required.

Some database systems have the idea of a catalog or a compound schema with two components. See examples below for how to pass in catalogs and schemas.

You can also specify a writePrefix. This is a short character string that will be added to any tables created in the writeSchema effectively a namespace in the schema just for your analysis. If the write_schema is a shared between multiple users setting a unique write_prefix ensures you do not overwrite existing tables and allows you to easily clean up tables by dropping all tables that start with the prefix.

Value

A list of dplyr database table references pointing to CDM tables

Examples

## Not run: 
library(CDMConnector)
con <- DBI::dbConnect(duckdb::duckdb(), eunomiaDir())

# minimal example
cdm <- cdmFromCon(con,
                  cdmSchema = "main",
                  writeSchema = "scratch")

# write prefix is optional but recommended if write_schema is shared
cdm <- cdmFromCon(con,
                  cdmSchema = "main",
                  writeSchema = "scratch",
                  writePrefix = "tmp_")

# Some database systems use catalogs or compound schemas.
# These can be specified as follows:
cdm <- cdmFromCon(con,
                  cdmSchema = "catalog.main",
                  writeSchema = "catalog.scratch",
                  writePrefix = "tmp_")

cdm <- cdmFromCon(con,
                  cdmSchema = c("my_catalog", "main"),
                  writeSchema = c("my_catalog", "scratch"),
                  writePrefix = "tmp_")

cdm <- cdmFromCon(con,
                  cdmSchema = c(catalog = "my_catalog", schema = "main"),
                  writeSchema = c(catalog = "my_catalog", schema = "scratch"),
                  writePrefix = "tmp_")

 DBI::dbDisconnect(con)

## End(Not run)

Subset a cdm object to a random sample of individuals

Description

cdmSample takes a cdm object and returns a new cdm that includes only a random sample of persons in the cdm. Only person_ids in both the person table and observation_period table will be considered.

Usage

cdmSample(cdm, n, seed = sample.int(1e+06, 1), name = "person_sample")

Arguments

Value

A modified cdm_reference object where all clinical tables are lazy queries pointing to subset

Examples

## Not run: 
library(CDMConnector)
library(dplyr, warn.conflicts = FALSE)

con <- DBI::dbConnect(duckdb::duckdb(), eunomiaDir())

cdm <- cdmFromCon(con, cdmSchema = "main")

cdmSampled <- cdmSample(cdm, n = 2)

cdmSampled$person %>%
  select(person_id)
#> # Source:   SQL [2 x 1]
#> # Database: DuckDB 0.6.1
#>   person_id
#>       <dbl>
#> 1       155
#> 2      3422

DBI::dbDisconnect(con, shutdown = TRUE)

## End(Not run)

Subset a cdm object to a set of persons

Description

cdmSubset takes a cdm object and a list of person IDs as input. It returns a new cdm that includes data only for persons matching the provided person IDs. Generated cohorts in the cdm will also be subset to the IDs provided.

Usage

cdmSubset(cdm, personId)

Arguments

Value

A modified cdm_reference object where all clinical tables are lazy queries pointing to subset

Examples

## Not run: 
library(CDMConnector)
library(dplyr, warn.conflicts = FALSE)

con <- DBI::dbConnect(duckdb::duckdb(), eunomiaDir())

cdm <- cdmFromCon(con, cdmSchema = "main")

cdm2 <- cdmSubset(cdm, personId = c(2, 18, 42))

cdm2$person %>%
  select(1:3)
#> # Source:   SQL [3 x 3]
#> # Database: DuckDB 0.6.1
#>   person_id gender_concept_id year_of_birth
#>       <dbl>             <dbl>         <dbl>
#> 1         2              8532          1920
#> 2        18              8532          1965
#> 3        42              8532          1909

DBI::dbDisconnect(con, shutdown = TRUE)

## End(Not run)

Subset a cdm to the individuals in one or more cohorts

Description

cdmSubset will return a new cdm object that contains lazy queries pointing to each of the cdm tables but subset to individuals in a generated cohort. Since the cdm tables are lazy queries, the subset operation will only be done when the tables are used. computeQuery can be used to run the SQL used to subset a cdm table and store it as a new table in the database.

Usage

cdmSubsetCohort(cdm, cohortTable = "cohort", cohortId = NULL, verbose = FALSE)

Arguments

Value

A modified cdm_reference with all clinical tables subset to just the persons in the selected cohorts.

Examples

## Not run: 
library(CDMConnector)
library(dplyr, warn.conflicts = FALSE)

con <- DBI::dbConnect(duckdb::duckdb(), eunomiaDir())

cdm <- cdmFromCon(con, cdmSchema = "main", writeSchema = "main")

# generate a cohort
path <- system.file("cohorts2", mustWork = TRUE, package = "CDMConnector")

cohortSet <- readCohortSet(path) %>%
  filter(cohort_name == "GIBleed_male")

# subset cdm to persons in the generated cohort
cdm <- generateCohortSet(cdm, cohortSet = cohortSet, name = "gibleed")

cdmGiBleed <- cdmSubsetCohort(cdm, cohortTable = "gibleed")

cdmGiBleed$person %>%
  tally()
#> # Source:   SQL [1 x 1]
#> # Database: DuckDB 0.6.1
#>       n
#>   <dbl>
#> 1   237

cdm$person %>%
  tally()
#> # Source:   SQL [1 x 1]
#> # Database: DuckDB 0.6.1
#>       n
#>   <dbl>
#> 1  2694


DBI::dbDisconnect(con, shutdown = TRUE)

## End(Not run)

Get cdm write schema

Description

Get cdm write schema

Usage

cdmWriteSchema(cdm)

Arguments

Value

The database write schema

Examples

## Not run: 
con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomiaDir())

cdm <- cdmFromCon(con = con, cdmName = "Eunomia",
                  cdmSchema =  "main", writeSchema = "main")

cdmWriteSchema(cdm)

DBI::dbDisconnect(con)

## End(Not run)

Execute dplyr query and save result in remote database

Description

This function is a wrapper around dplyr::compute that is tested on several database systems. It is needed to handle edge cases where dplyr::compute does not produce correct SQL.

Usage

computeQuery(
  x,
  name = uniqueTableName(),
  temporary = TRUE,
  schema = NULL,
  overwrite = TRUE,
  ...
)

Arguments

Value

A dplyr::tbl() reference to the newly created table.

Examples

## Not run: 
library(CDMConnector)

con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomiaDir())
cdm <- cdmFromCon(con, "main")

# create a temporary table in the remote database from a dplyr query
drugCount <- cdm$concept %>%
  dplyr::count(domain_id == "Drug") %>%
  computeQuery()

# create a permanent table in the remote database from a dplyr query
drugCount <- cdm$concept %>%
  dplyr::count(domain_id == "Drug") %>%
  computeQuery("tmp_table", temporary = FALSE, schema = "main")

DBI::dbDisconnect(con, shutdown = TRUE)

## End(Not run)

Copy a cdm object from one database to another

Description

It may be helpful to be able to easily copy a small test cdm from a local database to a remote for testing. copyCdmTo takes a cdm object and a connection. It copies the cdm to the remote database connection. CDM tables can be prefixed in the new database allowing for multiple cdms in a single shared database schema.

Usage

copyCdmTo(con, cdm, schema, overwrite = FALSE)

Arguments

Value

A cdm reference object pointing to the newly created cdm in the remote database

Add days or years to a date in a dplyr query

Description

This function must be "unquoted" using the "bang bang" operator (!!). See example.

Usage

dateadd(date, number, interval = "day")

Arguments

Value

Platform specific SQL that can be used in a dplyr query.

Examples

## Not run: 
con <- DBI::dbConnect(duckdb::duckdb())
date_tbl <- dplyr::copy_to(con, data.frame(date1 = as.Date("1999-01-01")),
                           name = "tmpdate", overwrite = TRUE, temporary = TRUE)

df <- date_tbl %>%
  dplyr::mutate(date2 = !!dateadd("date1", 1, interval = "year")) %>%
  dplyr::collect()

DBI::dbDisconnect(con, shutdown = TRUE)

## End(Not run)

Compute the difference between two days

Description

This function must be "unquoted" using the "bang bang" operator (!!). See example.

Usage

datediff(start, end, interval = "day")

Arguments

Value

Platform specific SQL that can be used in a dplyr query.

Examples

## Not run: 
con <- DBI::dbConnect(duckdb::duckdb())
date_tbl <- dplyr::copy_to(con, data.frame(date1 = as.Date("1999-01-01")),
                           name = "tmpdate", overwrite = TRUE, temporary = TRUE)

df <- date_tbl %>%
  dplyr::mutate(date2 = !!dateadd("date1", 1, interval = "year")) %>%
  dplyr::mutate(dif_years = !!datediff("date1", "date2", interval = "year")) %>%
  dplyr::collect()

DBI::dbDisconnect(con, shutdown = TRUE)

## End(Not run)

Extract the day, month or year of a date in a dplyr pipeline

Description

Extract the day, month or year of a date in a dplyr pipeline

Usage

datepart(date, interval = "year", dbms = NULL)

Arguments

Examples

## Not run: 
con <- DBI::dbConnect(duckdb::duckdb(), ":memory:")
date_tbl <- dplyr::copy_to(con,
                           data.frame(birth_date = as.Date("1993-04-19")),
                           name = "tmp",
                           temporary = TRUE)
df <- date_tbl %>%
  dplyr::mutate(year = !!datepart("birth_date", "year")) %>%
  dplyr::mutate(month = !!datepart("birth_date", "month")) %>%
  dplyr::mutate(day = !!datepart("birth_date", "day")) %>%
  dplyr::collect()
DBI::dbDisconnect(con, shutdown = TRUE)

## End(Not run)

Create a source for a cdm in a database.

Description

Create a source for a cdm in a database.

Usage

dbSource(con, writeSchema)

Arguments

Get the database management system (dbms) from a cdm_reference or DBI connection

Description

Get the database management system (dbms) from a cdm_reference or DBI connection

Usage

dbms(con)

Arguments

Value

A character string representing the dbms that can be used with SqlRender

Examples

## Not run: 
con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomiaDir())
cdm <- cdmFromCon(con)
dbms(cdm)
dbms(con)

## End(Not run)

Download Eunomia data files

Description

Download the Eunomia data files from https://github.com/darwin-eu/EunomiaDatasets

Usage

downloadEunomiaData(
  datasetName = "GiBleed",
  cdmVersion = "5.3",
  pathToData = Sys.getenv("EUNOMIA_DATA_FOLDER"),
  overwrite = FALSE
)

Arguments

Value

Invisibly returns the destination if the download was successful.

Examples

## Not run: 
downloadEunomiaData("GiBleed")

## End(Not run)

Drop table from a database backed cdm object

Description

Tables will be dropped from the write schema of the cdm.

Usage

## S3 method for class 'db_cdm'
dropTable(cdm, name)

Arguments

Create a copy of an example OMOP CDM dataset

Description

Eunomia is an OHDSI project that provides several example OMOP CDM datasets for testing and development. This function creates a copy of a Eunomia database in duckdb and returns the path to the new database file. If the dataset does not yet exist on the user's computer it will attempt to download the source data to the the path defined by the EUNOMIA_DATA_FOLDER environment variable.

Usage

eunomiaDir(
  datasetName = "GiBleed",
  cdmVersion = "5.3",
  databaseFile = tempfile(fileext = ".duckdb")
)

Arguments

Details

Most of the Eunomia datasets available in CDMConnector are from the Synthea project. Synthea is an open-source synthetic patient generator that models the medical history of synthetic patients. The Synthea datasets are generated using the Synthea tool and then converted to the OMOP CDM format using the OHDSI ETL-Synthea project https://ohdsi.github.io/ETL-Synthea/. Currently the synthea datasets are only available in the OMOP CDM v5.3 format. See https://synthetichealth.github.io/synthea/ for details on the Synthea project.

In addition to Synthea, the Eunomia project provides the CMS Synthetic Public Use Files (SynPUFs) in both 5.3 and 5.4 OMOP CDM formats. This data is synthetic US Medicare claims data mapped to OMOP CDM format. The OMOP CDM has a set of optional metadata tables, called Achilles tables, that include pre-computed analytics about the entire dataset such as record and person counts. The Eunomia Synpuf datasets include the Achilles tables.

Eunomia also provides empty cdms that can be used as a starting point for creating a new example CDM. This is useful for creating test data for studies or analytic packages. The empty CDM includes the vocabulary tables and all OMOP CDM tables but the clinical tables are empty and need to be populated with data. For additional information on creating small test CDM datasets see https://ohdsi.github.io/omock/ and https://darwin-eu.github.io/TestGenerator/.

To contribute synthetic observational health data to the Eunomia project please open an issue at https://github.com/OHDSI/Eunomia/issues/

Setup: To use the eunomiaDir function please set the EUNOMIA_DATA_FOLDER in your .Renviron file to a folder on your computer where the datasets will be downloaded to. This file can be opened by calling usethis::edit_r_environ().

Value

The file path to the new Eunomia dataset copy

Examples

## Not run: 

 # The defaults GiBleed dataset is a small dataset that is useful for testing
 library(CDMConnector)
 con <- DBI::dbConnect(duckdb::duckdb(), eunomiaDir())
 cdm <- cdmFromCon(con, "main", "main")
 cdmDisconnect(cdm)

 # Synpuf datasets include the Achilles tables
 con <- DBI::dbConnect(duckdb::duckdb(), eunomiaDir("synpuf-1k", "5.3"))
 cdm <- cdmFromCon(con, "main", "main", achillesSchema = "main")
 cdmDisconnect(cdm)

 # Currently the only 5.4 dataset is synpuf-1k
 con <- DBI::dbConnect(duckdb::duckdb(), eunomiaDir("synpuf-1k", "5.4"))
 cdm <- cdmFromCon(con, "main", "main", achillesSchema = "main")
 cdmDisconnect(cdm)


## End(Not run)

Has the Eunomia dataset been cached?

Description

Has the Eunomia dataset been cached?

Usage

eunomiaIsAvailable(datasetName = "GiBleed", cdmVersion = "5.3")

Arguments

Value

TRUE if the eunomia example dataset is available and FALSE otherwise

List the available example CDM datasets

Description

List the available example CDM datasets

Usage

exampleDatasets()

Value

A character vector with example CDM dataset identifiers

Examples

## Not run: 
library(CDMConnector)
exampleDatasets()[1]
#> [1] "GiBleed"

con <- DBI::dbConnect(duckdb::duckdb(), eunomiaDir("GiBleed"))
cdm <- cdmFromCon(con)

## End(Not run)

Generate a cohort set on a cdm object

Description

A "cohort_table" object consists of four components

• A remote table reference to an OHDSI cohort table with at least the columns: cohort_definition_id, subject_id, cohort_start_date, cohort_end_date. Additional columns are optional and some analytic packages define additional columns specific to certain analytic cohorts.

• A settings attribute which points to a remote table containing cohort settings including the names of the cohorts.

• An attrition attribute which points to a remote table with attrition information recorded during generation. This attribute is optional. Since calculating attrition takes additional compute it can be skipped resulting in a NULL attrition attribute.

• A cohortCounts attribute which points to a remote table containing cohort counts

Each of the three attributes are tidy tables. The implementation of this object is experimental and user feedback is welcome.

One key design principle is that cohort_table objects are created once and can persist across analysis execution but should not be modified after creation. While it is possible to modify a cohort_table object doing so will invalidate it and it's attributes may no longer be accurate.

Usage

generateCohortSet(
  cdm,
  cohortSet,
  name,
  computeAttrition = TRUE,
  overwrite = TRUE
)

Arguments

Examples

## Not run: 
library(CDMConnector)
con <- DBI::dbConnect(duckdb::duckdb(), eunomiaDir())
cdm <- cdmFromCon(con,
                  cdmSchema = "main",
                  writeSchema = "main")

cohortSet <- readCohortSet(system.file("cohorts2", package = "CDMConnector"))
cdm <- generateCohortSet(cdm, cohortSet, name = "cohort")

print(cdm$cohort)

attrition(cdm$cohort)
settings(cdm$cohort)
cohortCount(cdm$cohort)

## End(Not run)

Create a new generated cohort set from a list of concept sets

Description

Generate a new cohort set from one or more concept sets. Each concept set will result in one cohort and represent the time during which the concept was observed for each subject/person. Concept sets can be passed to this function as:

• A named list of numeric vectors, one vector per concept set

• A named list of Capr concept sets

Clinical observation records will be looked up in the respective domain tables using the vocabulary in the CDM. If a required domain table does not exist in the cdm object a warning will be given. Concepts that are not in the vocabulary or in the data will be silently ignored. If end dates are missing or do not exist, as in the case of the procedure and observation domains, the the start date will be used as the end date.

Usage

generateConceptCohortSet(
  cdm,
  conceptSet = NULL,
  name,
  limit = "first",
  requiredObservation = c(0, 0),
  end = "observation_period_end_date",
  subsetCohort = NULL,
  subsetCohortId = NULL,
  overwrite = TRUE
)

Arguments

Value

A cdm reference object with the new generated cohort set table added

Helper for working with compound schemas

Description

This is similar to dbplyr::in_schema but has been tested across multiple database platforms. It only exists to work around some of the limitations of dbplyr::in_schema.

Usage

inSchema(schema, table, dbms = NULL)

Arguments

Value

A DBI::Id that represents a qualified table and schema

List tables in a schema

Description

DBI::dbListTables can be used to get all tables in a database but not always in a specific schema. listTables will list tables in a schema.

Usage

listTables(con, schema = NULL)

Arguments

Value

A character vector of table names

Examples

## Not run: 
con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomiaDir())
listTables(con, schema = "main")

## End(Not run)

Read a set of cohort definitions into R

Description

A "cohort set" is a collection of cohort definitions. In R this is stored in a dataframe with cohort_definition_id, cohort_name, and cohort columns. On disk this is stored as a folder with a CohortsToCreate.csv file and one or more json files. If the CohortsToCreate.csv file is missing then all of the json files in the folder will be used, cohort_definition_id will be automatically assigned in alphabetical order, and cohort_name will match the file names.

Usage

readCohortSet(path)

Arguments

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

dplyr

compute

omopgenerics

attrition, bind, cdmDisconnect, cdmFromTables, cdmName, cdmSelect, cdmVersion, cohortCodelist, cohortCount, dropSourceTable, dropTable, insertCdmTo, insertTable, listSourceTables, newCohortTable, readSourceTable, recordCohortAttrition, settings, uniqueTableName

Require eunomia to be available. The function makes sure that you can later create a eunomia database with eunomiaDir().

Description

Require eunomia to be available. The function makes sure that you can later create a eunomia database with eunomiaDir().

Usage

requireEunomia(datasetName = "GiBleed", cdmVersion = "5.3")

Arguments

Value

Path to eunomia database.

Extract CDM metadata

Description

Extract the name, version, and selected record counts from a cdm.

Usage

snapshot(cdm)

Arguments

Value

A named list of attributes about the cdm including selected fields from the cdm_source table and record counts from the person and observation_period tables

Examples

## Not run: 
library(CDMConnector)
con <- DBI::dbConnect(duckdb::duckdb(), eunomiaDir())
cdm <- cdmFromCon(con, "main")
snapshot(cdm)

DBI::dbDisconnect(con, shutdown = TRUE)

## End(Not run)

Quantile calculation using dbplyr

Description

This function provides DBMS independent syntax for quantiles estimation. Can be used by itself or in combination with mutate() when calculating other aggregate metrics (min, max, mean).

summarise_quantile(), summarize_quantile(), summariseQuantile() and summarizeQuantile() are synonyms.

Usage

summariseQuantile(.data, x = NULL, probs, nameSuffix = "value")

Arguments

Details

Implemented quantiles estimation algorithm returns values analogous to ⁠quantile{stats}⁠ with argument type = 1. See discussion in Hyndman and Fan (1996). Results differ from PERCENTILE_CONT natively implemented in various DBMS, where returned values are equal to ⁠quantile{stats}⁠ with default argument type = 7

Value

An object of the same type as '.data'

Examples

## Not run: 
con <- DBI::dbConnect(duckdb::duckdb())
mtcars_tbl <- dplyr::copy_to(con, mtcars, name = "tmp", overwrite = TRUE, temporary = TRUE)

df <- mtcars_tbl %>%
 dplyr::group_by(cyl) %>%
 dplyr::mutate(mean = mean(mpg, na.rm = TRUE)) %>%
 summariseQuantile(mpg, probs = c(0, 0.2, 0.4, 0.6, 0.8, 1),
                   nameSuffix = "quant") %>%
 dplyr::collect()

DBI::dbDisconnect(con, shutdown = TRUE)

## End(Not run)

Quantile calculation using dbplyr

Description

This function provides DBMS independent syntax for quantile estimation. Some database systems do not have a quantile function. The SQL generated by summarizeQuantile2 should work on all supported database systems. This function can be added to a dplyr pipeline and adds an additional query to the input. No computation is triggered by summarizeQuantile2 if the input is a tbl reference to a database table.

Usage

summariseQuantile2(.data, x, probs, nameSuffix = "{x}")

Arguments

Details

Implemented quantiles estimation algorithm returns values analogous to ⁠quantile{stats}⁠ with argument type = 1. See discussion in Hyndman and Fan (1996). Results differ from PERCENTILE_CONT natively implemented in various DBMS, where returned values are equal to ⁠quantile{stats}⁠ with default argument type = 7

Value

A lazy query with quantile calculation added. The result (after computation) will have one row per combination of grouping variables and one column for every variable/quantile combination. (see examples)

Examples

## Not run: 
con <- DBI::dbConnect(duckdb::duckdb())
mtcars_tbl <- dplyr::copy_to(con, mtcars, name = "tmp", overwrite = TRUE, temporary = TRUE)

# quantiles for a single column
mtcars_tbl %>%
  dplyr::group_by(cyl) %>%
  dplyr::mutate(mean = mean(mpg, na.rm = TRUE)) %>%
  summariseQuantile2("mpg", probs = c(0, 0.2, 0.4, 0.6, 0.8, 1),  nameSuffix = "quant") %>%
  dplyr::collect()

#> cyl  p0_quant p20_quant p40_quant p60_quant p80_quant p100_quant
#>   6      17.8      18.1      19.2      21        21         21.4
#>   8      10.4      13.3      15        15.5      17.3       19.2
#>   4      21.4      22.8      24.4      27.3      30.4       33.9

# multiple columns
mtcars_tbl %>%
  dplyr::group_by(cyl) %>%
  dplyr::mutate(mean = mean(mpg, na.rm = TRUE)) %>%
  summariseQuantile2(c("mpg", "hp", "wt"), probs = c(0.2, 0.8),  nameSuffix = "{x}_quant") %>%
  dplyr::collect()

#>  cyl p20_mpg_quant p80_mpg_quant p20_hp_quant p80_hp_quant p20_wt_quant p80_wt_quant
#>    4          22.8          30.4           65           97         1.84         2.78
#>    6          18.1          21            110          123         2.77         3.44
#>    8          13.3          17.3          175          245         3.44         5.25

DBI::dbDisconnect(con, shutdown = TRUE)

## End(Not run)

CDM table selection helper

Description

The OMOP CDM tables are grouped together and the tblGroup function allows users to easily create a CDM reference including one or more table groups.

Usage

tblGroup(group)

Arguments

Details

The "default" table group is meant to capture the most commonly used set of CDM tables. Currently the "default" group is: person, observation_period, visit_occurrence, visit_detail, condition_occurrence, drug_exposure, procedure_occurrence, device_exposure, measurement, observation, death, note, note_nlp, specimen, fact_relationship, location, care_site, provider, payer_plan_period, cost, drug_era, dose_era, condition_era, concept, vocabulary, concept_relationship, concept_ancestor, concept_synonym, drug_strength

Value

A character vector of CDM tables names in the groups

Examples

## Not run: 
con <- DBI::dbConnect(RPostgres::Postgres(),
                      dbname = "cdm",
                      host = "localhost",
                      user = "postgres",
                      password = Sys.getenv("PASSWORD"))

cdm <- cdmFromCon(con, cdmName = "test", cdmSchema = "public") %>%
  cdmSelectTbl(tblGroup("vocab"))

## End(Not run)

Get the CDM version

Description

Extract the CDM version attribute from a cdm_reference object

Usage

version(cdm)

Arguments

Value

"5.3" or "5.4"

Examples

## Not run: 
library(CDMConnector)
con <- DBI::dbConnect(duckdb::duckdb(), eunomiaDir())
cdm <- cdmFromCon(con, cdmSchema = "main", writeSchema = "main")
version(cdm)

DBI::dbDisconnect(con, shutdown = TRUE)

## End(Not run)