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omopgenerics

Package overview

The omopgenerics package provides definitions of core classes and methods used by analytic pipelines that query the OMOP common data model.

#> Warning in citation("omopgenerics"): could not determine year for
#> 'omopgenerics' from package DESCRIPTION file
#> To cite package 'omopgenerics' in publications use:
#> 
#>   Català M, Burn E (2025). _omopgenerics: Methods and Classes for the
#>   OMOP Common Data Model_. R package version 1.3.2,
#>   <https://darwin-eu.github.io/omopgenerics/>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {omopgenerics: Methods and Classes for the OMOP Common Data Model},
#>     author = {Martí Català and Edward Burn},
#>     note = {R package version 1.3.2},
#>     url = {https://darwin-eu.github.io/omopgenerics/},
#>   }

If you find the package useful in supporting your research study, please consider citing this package.

Installation

You can install the development version of OMOPGenerics from GitHub with:

install.packages("pak")
pak::pkg_install("darwin-eu/omopgenerics")

And load it using the library command:

library(omopgenerics)
library(dplyr)

Core classes and methods

CDM Reference

A cdm reference is a single R object that represents OMOP CDM data. The tables in the cdm reference may be in a database, but a cdm reference may also contain OMOP CDM tables that are in dataframes/tibbles or in arrow. In the latter case the cdm reference would typically be a subset of an original cdm reference that has been derived as part of a particular analysis.

omopgenerics contains the class definition of a cdm reference and a dataframe implementation. For creating a cdm reference using a database, see the CDMConnector package (https://darwin-eu.github.io/CDMConnector/).

A cdm object can contain four type of tables:

  • Standard tables:
omopTables()
#>  [1] "person"                "observation_period"    "visit_occurrence"     
#>  [4] "visit_detail"          "condition_occurrence"  "drug_exposure"        
#>  [7] "procedure_occurrence"  "device_exposure"       "measurement"          
#> [10] "observation"           "death"                 "note"                 
#> [13] "note_nlp"              "specimen"              "fact_relationship"    
#> [16] "location"              "care_site"             "provider"             
#> [19] "payer_plan_period"     "cost"                  "drug_era"             
#> [22] "dose_era"              "condition_era"         "metadata"             
#> [25] "cdm_source"            "concept"               "vocabulary"           
#> [28] "domain"                "concept_class"         "concept_relationship" 
#> [31] "relationship"          "concept_synonym"       "concept_ancestor"     
#> [34] "source_to_concept_map" "drug_strength"         "cohort_definition"    
#> [37] "attribute_definition"  "concept_recommended"

Each one of the tables has a required columns. For example, for the person table this are the required columns:

omopColumns(table = "person")
#>  [1] "person_id"                   "gender_concept_id"          
#>  [3] "year_of_birth"               "month_of_birth"             
#>  [5] "day_of_birth"                "birth_datetime"             
#>  [7] "race_concept_id"             "ethnicity_concept_id"       
#>  [9] "location_id"                 "provider_id"                
#> [11] "care_site_id"                "person_source_value"        
#> [13] "gender_source_value"         "gender_source_concept_id"   
#> [15] "race_source_value"           "race_source_concept_id"     
#> [17] "ethnicity_source_value"      "ethnicity_source_concept_id"
  • Cohort tables We can see the cohort-related tables and their required columns.
cohortTables()
#> [1] "cohort"           "cohort_set"       "cohort_attrition" "cohort_codelist"
cohortColumns(table = "cohort")
#> [1] "cohort_definition_id" "subject_id"           "cohort_start_date"   
#> [4] "cohort_end_date"

In addition, cohorts are defined in terms of a generatedCohortSet class. For more details on this class definition see the corresponding vignette.

  • Achilles tables The Achilles R package generates descriptive statistics about the data contained in the OMOP CDM. Again, we can see the tables created and their required columns.
achillesTables()
#> [1] "achilles_analysis"     "achilles_results"      "achilles_results_dist"
achillesColumns(table = "achilles_results")
#> [1] "analysis_id" "stratum_1"   "stratum_2"   "stratum_3"   "stratum_4"  
#> [6] "stratum_5"   "count_value"
  • Other tables, these other tables can have any format.

Any table to be part of a cdm object has to fulfill 4 conditions:

  • All must share a common source.

  • The name of the tables must be lowercase.

  • The name of the column names of each table must be lowercase.

  • person and observation_period must be present.

Concept set

A concept set can be represented as either a codelist or a concept set expression. A codelist is a named list, with each item of the list containing specific concept IDs.

condition_codes <- list(
  "diabetes" = c(201820L, 4087682L, 3655269L),
  "asthma" = 317009L
)
condition_codes <- newCodelist(condition_codes)

condition_codes
#> 
#> ── 2 codelists ─────────────────────────────────────────────────────────────────
#> 
#> - asthma (1 codes)
#> - diabetes (3 codes)

Meanwhile, a concept set expression provides a high-level definition of concepts that, when applied to a specific OMOP CDM vocabulary version (by making use of the concept hierarchies and relationships), will result in a codelist.

condition_cs <- list(
  "diabetes" = dplyr::tibble(
    "concept_id" = c(201820L, 4087682L),
    "excluded" = c(FALSE, FALSE),
    "descendants" = c(TRUE, FALSE),
    "mapped" = c(FALSE, FALSE)
  ),
  "asthma" = dplyr::tibble(
    "concept_id" = 317009L,
    "excluded" = FALSE,
    "descendants" = FALSE,
    "mapped" = FALSE
  )
)
condition_cs <- newConceptSetExpression(condition_cs)

condition_cs
#> 
#> ── 2 concept set expressions ───────────────────────────────────────────────────
#> 
#> - asthma (1 concept criteria)
#> - diabetes (2 concept criteria)

A cohort table

A cohort is a set of persons who satisfy one or more inclusion criteria for a duration of time and, when defined, this table in a cdm reference has a cohort table class. Cohort tables are then associated with attributes such as settings and attrition.

person <- tibble(
  person_id = 1L,
  gender_concept_id = 0L,
  year_of_birth = 1990L,
  race_concept_id = 0L, 
  ethnicity_concept_id = 0L
)
observation_period <- dplyr::tibble(
  observation_period_id = 1L, 
  person_id = 1L,
  observation_period_start_date = as.Date("2000-01-01"),
  observation_period_end_date = as.Date("2023-12-31"),
  period_type_concept_id = 0L
)
diabetes <- tibble(
  cohort_definition_id = 1L, 
  subject_id = 1L,
  cohort_start_date = as.Date("2020-01-01"),
  cohort_end_date = as.Date("2020-01-10")
)

cdm <- cdmFromTables(
  tables = list(
    "person" = person,
    "observation_period" = observation_period,
    "diabetes" = diabetes
  ),
  cdmName = "example_cdm"
)
cdm$diabetes <- newCohortTable(cdm$diabetes)

cdm$diabetes
#> # A tibble: 1 × 4
#>   cohort_definition_id subject_id cohort_start_date cohort_end_date
#>                  <int>      <int> <date>            <date>         
#> 1                    1          1 2020-01-01        2020-01-10
settings(cdm$diabetes)
#> # A tibble: 1 × 2
#>   cohort_definition_id cohort_name
#>                  <int> <chr>      
#> 1                    1 cohort_1
attrition(cdm$diabetes)
#> # A tibble: 1 × 7
#>   cohort_definition_id number_records number_subjects reason_id reason          
#>                  <int>          <int>           <int>     <int> <chr>           
#> 1                    1              1               1         1 Initial qualify…
#> # ℹ 2 more variables: excluded_records <int>, excluded_subjects <int>
cohortCount(cdm$diabetes)
#> # A tibble: 1 × 3
#>   cohort_definition_id number_records number_subjects
#>                  <int>          <int>           <int>
#> 1                    1              1               1

Summarised result

A summarised result provides a standard format for the results of an analysis performed against data mapped to the OMOP CDM.

For example this format is used when we get a summary of the cdm as a whole

summary(cdm) |>
  glimpse()
#> Rows: 13
#> Columns: 13
#> $ result_id        <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
#> $ cdm_name         <chr> "example_cdm", "example_cdm", "example_cdm", "example…
#> $ group_name       <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ group_level      <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ strata_name      <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ strata_level     <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ variable_name    <chr> "snapshot_date", "person_count", "observation_period_…
#> $ variable_level   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
#> $ estimate_name    <chr> "value", "count", "count", "source_name", "version", …
#> $ estimate_type    <chr> "date", "integer", "integer", "character", "character…
#> $ estimate_value   <chr> "2025-10-12", "1", "1", "", NA, "5.3", "", "", "", ""…
#> $ additional_name  <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ additional_level <chr> "overall", "overall", "overall", "overall", "overall"…

and also when we summarise a cohort

summary(cdm$diabetes) |>
  glimpse()
#> `cohort_definition_id` casted to character.
#> `cohort_definition_id` casted to character.
#> Rows: 6
#> Columns: 13
#> $ result_id        <int> 1, 1, 2, 2, 2, 2
#> $ cdm_name         <chr> "example_cdm", "example_cdm", "example_cdm", "example…
#> $ group_name       <chr> "cohort_name", "cohort_name", "cohort_name", "cohort_…
#> $ group_level      <chr> "cohort_1", "cohort_1", "cohort_1", "cohort_1", "coho…
#> $ strata_name      <chr> "overall", "overall", "reason", "reason", "reason", "…
#> $ strata_level     <chr> "overall", "overall", "Initial qualifying events", "I…
#> $ variable_name    <chr> "number_records", "number_subjects", "number_records"…
#> $ variable_level   <chr> NA, NA, NA, NA, NA, NA
#> $ estimate_name    <chr> "count", "count", "count", "count", "count", "count"
#> $ estimate_type    <chr> "integer", "integer", "integer", "integer", "integer"…
#> $ estimate_value   <chr> "1", "1", "1", "1", "0", "0"
#> $ additional_name  <chr> "overall", "overall", "reason_id", "reason_id", "reas…
#> $ additional_level <chr> "overall", "overall", "1", "1", "1", "1"

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Version

Install

install.packages('omopgenerics')

Monthly Downloads

1,432

Version

1.3.3

License

Apache License (>= 2)

Maintainer

Marti Catala

Last Published

November 11th, 2025

Functions in omopgenerics (1.3.3)

assertTrue

Assert that an expression is TRUE.
attrition

Get attrition from an object.
cdmFromTables

Create a cdm object from local tables
cdmSelect

Restrict the cdm object to a subset of tables.
cdmReference

Get the cdm_reference of a cdm_table.
cdmDisconnect

Disconnect from a cdm object.
cdmClasses

Separate the cdm tables in classes
cdmSourceType

Get the source type of a cdm_reference object.
cdmSource

Get the cdmSource of an object.
collect.cohort_table

To collect a cohort_table object.
combineStrata

Provide all combinations of strata levels.
cohortCount

Get cohort counts from a cohort_table object.
cohortTables

Cohort tables that a cdm reference can contain in the OMOP Common Data Model.
cdmName

Get the name of a cdm_reference associated object
cohortColumns

Required columns for a generated cohort set.
checkCohortRequirements

Check whether a cohort table satisfies requirements
cdmTableFromSource

This is an internal developer focused function that creates a cdm_table from a table that shares the source but it is not a cdm_table. Please use insertTable if you want to insert a table to a cdm_reference object.
collect.cdm_reference

Retrieves the cdm reference into a local cdm.
assertTable

Assert that an object is a table.
cdmVersion

Get the version of an object.
assertNumeric

Assert that an object is a numeric.
createIndexes

Create the missing indexes
emptyCodelist

Empty codelist object.
emptyCodelistWithDetails

Empty codelist object.
dropSourceTable

Drop a table from a cdm object.
emptyAchillesTable

Create an empty achilles table
emptyCdmReference

Create an empty cdm_reference
cohortCodelist

Get codelist from a cohort_table object.
compute.cdm_table

Store results in a table.
dropTable

Drop a table from a cdm object. [Deprecated]
exportSummarisedResult

Export a summarised_result object to a csv file.
exportConceptSetExpression

Export a concept set expression.
emptyCohortTable

Create an empty cohort_table object
getPersonIdentifier

Get the column name with the person identifier from a table (either subject_id or person_id), it will throw an error if it contains both or neither.
importCodelist

Import a codelist.
existingIndexes

Existing indexes in a cdm object
estimateTypeChoices

Choices that can be present in estimate_type column.
filterSettings

Filter a <summarised_result> using the settings
createLogFile

Create a log file
getCohortId

Get the cohort definition id of a certain name
emptyConceptSetExpression

Empty concept_set_expression object.
expectedIndexes

Expected indexes in a cdm object
getCohortName

Get the cohort name of a certain cohort definition id
exportCodelist

Export a codelist object.
groupColumns

Identify variables in group_name column
emptySummarisedResult

Empty summarised_result object.
emptyOmopTable

Create an empty omop table
newAchillesTable

Create an achilles table from a cdm_table.
isTableEmpty

Check if a table is empty or not
isResultSuppressed

To check whether an object is already suppressed to a certain min cell count.
filterStrata

Filter the strata_name-strata_level pair in a summarised_result
createTableIndex

Create a table index
newCdmSource

Create a cdm source object.
newCdmTable

Create an cdm table.
filterAdditional

Filter the additional_name-additional_level pair in a summarised_result
insertTable

Insert a table to a cdm object.
insertFromSource

Convert a table that is not a cdm_table but have the same original source to a cdm_table. This Table is not meant to be used to insert tables in the cdm, please use insertTable instead.
newCohortTable

cohort_table objects constructor.
newCdmReference

cdm_reference objects constructor
newConceptSetExpression

'concept_set_expression' object constructor
newSummarisedResult

'summarised_results' object constructor
importConceptSetExpression

Import a concept set expression.
filterGroup

Filter the group_name-group_level pair in a summarised_result
newCodelistWithDetails

'codelist' object constructor
newLocalSource

A new local source for the cdm
newOmopTable

Create an omop table from a cdm table.
newCodelist

'codelist' object constructor
numberRecords

Count the number of records that a cdm_table has.
omopTables

Standard tables that a cdm reference can contain in the OMOP Common Data Model.
omopTableFields

Return a table of omop cdm fields informations
omopgenerics-package

omopgenerics: Methods and Classes for the OMOP Common Data Model
omopDataFolder

Check or set the OMOP_DATA_FOLDER where the OMOP related data is stored.
listSourceTables

List tables that can be accessed though a cdm object.
insertCdmTo

Insert a cdm_reference object to a different source.
logMessage

Log a message to a logFile
importSummarisedResult

Import a set of summarised results.
print.codelist

Print a codelist
recordCohortAttrition

Update cohort attrition.
settings.summarised_result

Get settings from a summarised_result object.
resultColumns

Required columns that the result tables must have.
readSourceTable

Read a table from the cdm_source and add it to to the cdm.
settingsColumns

Identify settings columns of a <summarised_result>
resultPackageVersion

Check if different packages version are used for summarise_results object
print.conceptSetExpression

Print a concept set expression
reexports

Objects exported from other packages
print.codelist_with_details

Print a codelist with details
settings

Get settings from an object.
[[.cdm_reference

Subset a cdm reference object.
[[<-.cdm_reference

Assign a table to a cdm reference.
settings.cohort_table

Get cohort settings from a cohort_table object.
summariseLogFile

Summarise and extract the information of a log file into a summarised_result object.
strataColumns

Identify variables in strata_name column
suppress.summarised_result

Function to suppress counts in result objects
tmpPrefix

Create a temporary prefix for tables, that contains a unique prefix that starts with tmp.
summary.cdm_reference

Summary a cdm reference
summary.cdm_source

Summarise a cdm_source object
suppress

Function to suppress counts in result objects
statusIndexes

Status of the indexes
splitStrata

Split strata_name and strata_level columns
tableName

Get the table name of a cdm_table.
toSnakeCase

Convert a character vector to snake case
uniqueTableName

Create a unique table name
uniteGroup

Unite one or more columns in group_name-group_level format
uniteStrata

Unite one or more columns in strata_name-strata_level format
transformToSummarisedResult

Create a <summarised_result> object from a data.frame, given a set of specifications.
splitAll

Split all pairs name-level into columns.
tidy.summarised_result

Turn a <summarised_result> object into a tidy tibble
uniteAdditional

Unite one or more columns in additional_name-additional_level format
validateCohortIdArgument

Validate cohortId argument. CohortId can either be a cohort_definition_id value, a cohort_name or a tidyselect expression referinc to cohort_names. If you want to support tidyselect expressions please use the function as: validateCohortIdArgument({{cohortId}}, cohort).
validateCohortArgument

Validate a cohort table input.
validateColumn

Validate whether a variable points to a certain exiting column in a table.
validateConceptSetArgument

Validate conceptSet argument. It can either be a list, a codelist, a concept set expression or a codelist with details. The output will always be a codelist.
validateAchillesTable

Validate if a cdm_table is a valid achilles table.
validateNameLevel

Validate if two columns are valid Name-Level pair.
validateAgeGroupArgument

Validate the ageGroup argument. It must be a list of two integerish numbers lower age and upper age, both of the must be greater or equal to 0 and lower age must be lower or equal to the upper age. If not named automatic names will be given in the output list.
validateNameArgument

Validate name argument. It must be a snake_case character vector. You can add the a cdm object to check name is not already used in that cdm.
tableSource

Get the table source of a cdm_table.
numberSubjects

Count the number of subjects that a cdm_table has.
splitGroup

Split group_name and group_level columns
validateOmopTable

Validate an omop_table
tidyColumns

Identify tidy columns of a <summarised_result>
validateResultArgument

Validate if a an object is a valid 'summarised_result' object.
splitAdditional

Split additional_name and additional_level columns
print.cdm_reference

Print a CDM reference object
summary.cohort_table

Summary a generated cohort set
omopColumns

Required columns that the standard tables in the OMOP Common Data Model must have.
pivotEstimates

Set estimates as columns
sourceType

Get the source type of an object.
summary.summarised_result

Summary a summarised_result
uniqueId

Get a unique Identifier with a certain number of characters and a prefix.
validateStrataArgument

To validate a strata list. It makes sure that elements are unique and point to columns in table.
validateCdmArgument

Validate if an object in a valid cdm_reference.
validateCdmTable

Validate if a table is a valid cdm_table object.
validateNameStyle

Validate nameStyle argument. If any of the element in ... has length greater than 1 it must be contained in nameStyle. Note that snake case notation is used.
validateNewColumn

Validate a new column of a table
validateWindowArgument

Validate a window argument. It must be a list of two elements (window start and window end), both must be integerish and window start must be lower or equal than window end.
assertCharacter

Assert that an object is a character and fulfill certain conditions.
achillesColumns

Required columns for each of the achilles result tables
additionalColumns

Identify variables in additional_name column
assertLogical

Assert that an object is a logical.
addSettings

Add settings columns to a <summarised_result> object
assertList

Assert that an object is a list.
achillesTables

Names of the tables that contain the results of achilles analyses
bind.cohort_table

Bind two or more cohort tables
bind.summarised_result

Bind two or summarised_result objects
attrition.cohort_table

Get cohort attrition from a cohort_table object.
bind

Bind two or more objects of the same class.
assertChoice

Assert that an object is within a certain oprtions.
$<-.cdm_reference

Assign an table to a cdm reference.
$.cdm_reference

Subset a cdm reference object.
assertClass

Assert that an object has a certain class.
assertDate

Assert Date