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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 (????). _omopgenerics: Methods and Classes for the
#>   OMOP Common Data Model_. R package version 1.3.7,
#>   <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 1.3.7},
#>     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 data frames/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 data frame 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 types 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 table has required columns. For example, these are the required columns for the person table:

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, which can have any format.

Any table that is part of a cdm object has to satisfy four conditions:

  • All must share a common source.

  • Table names must be lowercase.

  • Column names in 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 people who satisfy one or more inclusion criteria for a period of time. When represented in a cdm reference, this table has the 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$diabetes)
#> # A tibble: 1 × 8
#>   cohort_definition_id cohort_name number_records number_subjects reason_id
#>                  <int> <chr>                <int>           <int>     <int>
#> 1                    1 cohort_1                 1               1         1
#> # ℹ 3 more variables: reason <chr>, excluded_records <int>,
#> #   excluded_subjects <int>
cohortCount(cdm$diabetes)
#> # A tibble: 1 × 4
#>   cohort_definition_id cohort_name number_records number_subjects
#>                  <int> <chr>                <int>           <int>
#> 1                    1 cohort_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> "2026-06-02", "1", "1", "", NA, "5.3", "", "", "", ""…
#> $ additional_name  <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ additional_level <chr> "overall", "overall", "overall", "overall", "overall"…

It is also used when we summarise a cohort:

summary(cdm$diabetes) |>
  glimpse()
#> `cohort_definition_id` cast to character.
#> `cohort_definition_id` cast 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

1,681

Version

1.4.1

License

Apache License (>= 2)

Maintainer

Marti Catala

Last Published

July 23rd, 2026

Functions in omopgenerics (1.4.1)

cdmDisconnect

Disconnect from a cdm object.
cdmSourceType

Get the source type of a cdm_reference object.
$<-.cdm_reference

Assign a table to a cdm reference.
cdmReference

Get the cdm_reference of a cdm_table.
cdmName

Get or set the name of a cdm_reference associated object
bind.summarised_result

Bind two or summarised_result objects
checkCohortRequirements

Check whether a cohort table satisfies requirements
$.cdm_reference

Subset a cdm reference object.
cdmSource

Get the cdmSource of an object.
cdmSelect

Restrict the cdm object to a subset of tables.
emptyCohortTable

Create an empty cohort_table object
cdmFromTables

Create a cdm object from local tables
cdmOrTableDoc

Helper for consistent documentation of cdm reference or table arguments.
cliCallDoc

Helper for consistent documentation of call arguments.
castDoc

Helper for consistent documentation of cast arguments.
codelistDoc

Helper for consistent documentation of codelist objects.
cdmVersionArgumentDoc

Helper for consistent documentation of CDM version arguments.
cdmIndexDoc

Helper for consistent documentation of CDM index functions.
cdmDoc

Helper for consistent documentation of cdm.
emptyConceptSetExpression

Empty concept_set_expression object.
cdmNameDoc

Helper for consistent documentation of CDM names.
cohortTables

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

Helper for consistent documentation of cdm_table objects.
exportCodelist

Export a codelist object.
compute.cdm_table

Store results in a table.
exportCodeSearch

Export a code_search object into an Excel spreadsheet
cohortCodelist

Get codelist from a cohort_table object.
codelistWithDetailsDoc

Helper for consistent documentation of codelist_with_details objects.
emptyOmopTable

Create an empty omop table
exportCodelistWithDetails

Export a codelist with details object.
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.
cdmVersion

Get the version of an object.
emptyDoc

Helper for consistent documentation of empty arguments.
conceptCdmDoc

Helper for consistent documentation of cdm concept validation.
getCohortId

Get the cohort definition id of a certain name
getCohortName

Get the cohort name of a certain cohort definition id
insertTable

Insert a table into a cdm object.
guessCdmVersion

Guess the OMOP CDM version
importSummarisedResult

Import a set of summarised results.
newCdmSource

Create a cdm source object.
insertCdmTo

Insert a cdm_reference object to a different source.
conceptSetExpressionDoc

Helper for consistent documentation of concept_set_expression objects.
newAchillesTable

Create an achilles table from a cdm_table.
newCdmReference

cdm_reference objects constructor
expectedIndexes

Expected indexes in a cdm object
emptyCodelistWithDetails

Empty codelist 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.
cohortDoc

Helper for consistent documentation of cohort_table objects.
newCdmTable

Create an cdm table.
filterSettings

Filter a <summarised_result> using the settings
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.
createLogFile

Create a log file
dropTable

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

Create the missing indexes
newConceptSetExpression

'concept_set_expression' object constructor
collect.cohort_table

To collect a cohort_table object.
emptyCodeSearch

Empty code search object
collect.cdm_reference

Retrieve the cdm reference into a local cdm.
exportFileDoc

Helper for consistent documentation of exported files.
createTableIndex

Create a table index
dropSourceTable

Drop a table from a cdm object.
newCodelistWithDetails

'codelist' object constructor
print.code_search

Print a code search
newCohortTable

cohort_table objects constructor.
cohortValidationChecksDoc

Helper for consistent documentation of cohort validation checks.
isTableEmpty

Check if a table is empty or not
emptyCodelist

Empty codelist object.
resultColumns

Required columns that the result tables must have.
resultPackageVersion

Check if different package versions are used for a summarised_result object
emptyCdmReference

Create an empty cdm_reference
cohortColumns

Required columns for a generated cohort set.
cohortCount

Get cohort counts from a cohort_table object.
estimateTypeChoices

Choices that can be present in estimate_type column.
combineStrata

Provide all combinations of strata levels.
importCodeSearch

Import a code_search object from an Excel spreadsheet
compareOmopTableFields

Compare the fields of two different OMOP CDM versions
exportSummarisedResult

Export a summarised_result object to a CSV file.
filterAdditional

Filter the additional_name-additional_level pair in a summarised_result
settings

Get settings from an object.
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.
newSummarisedResult

summarised_result object constructor
splitGroup

Split group_name and group_level columns
print.codelist

Print a codelist
splitAll

Split all pairs name-level into columns.
recursiveDoc

Helper for consistent documentation of recursive file imports.
logMessage

Log a message to a logFile
omopTableFields

Return a table of omop cdm field information
listSourceTables

List tables that can be accessed through a cdm object.
emptyAchillesTable

Create an empty achilles table
omopColumns

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

Filter a <summarised_result> automatically
importCodelist

Import a codelist.
exportConceptSetExpression

Export a concept set expression.
filterGroup

Filter the group_name-group_level pair in a summarised_result
importCodelistWithDetails

Import a codelist with details.
settings.cohort_table

Get cohort settings from a cohort_table object.
emptySummarisedResult

Empty summarised_result object.
isResultSuppressed

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

A new local source for the cdm
resultType

Get the result_type(s) defined in a certain package
groupColumns

Identify variables in group_name column
sourceType

Get the source type of an object.
newReadOnlySource

A new read-only source for the cdm
print.codelist_with_details

Print a codelist with details
summary.cdm_source

Summarise a cdm_source object
tmpPrefix

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

Summarise a generated cohort set
existingIndexes

Existing indexes in a cdm object
print.concept_set_expression

Print a concept set expression
emptyTableNameDoc

Helper for consistent documentation of empty table names.
omopgenerics-package

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

Helper for consistent documentation of imported files.
reexports

Objects exported from other packages
suppress

Function to suppress counts in result objects
filterStrata

Filter the strata_name-strata_level pair in a summarised_result
strataColumns

Identify variables in strata_name column
omopDataFolder

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

Convert a character vector to snake case
uniteGroup

Unite one or more columns in group_name-group_level format
newCodelist

'codelist' object constructor
importConceptSetExpression

Import a concept set expression.
suppress.summarised_result

Function to suppress counts in result objects
overwriteDoc

Helper for consistent documentation of overwrite arguments.
newCodeSearch

Create a new code_search object
pivotEstimates

Set estimates as columns
newOmopTable

Create an omop table from a cdm table.
summariseLogFile

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

Count the number of subjects that a cdm_table has.
[[.cdm_reference

Subset a cdm reference object.
uniteStrata

Unite one or more columns in strata_name-strata_level format
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.
validateCdmArgument

Validate if an object in a valid cdm_reference.
tableSource

Get the table source of a cdm_table.
validateNameArgument

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

Validate if a cdm_table is a valid achilles 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.
unusedDotsDoc

Helper for consistent documentation of unused dots.
tableName

Get the table name of a cdm_table.
omopCdmVersionDoc

Helper for consistent documentation of OMOP CDM versions.
searchStrategy

Get the search strategy used to create a code_search
print.cdm_reference

Print a CDM reference object
recordCohortAttrition

Update cohort attrition.
transformToSummarisedResult

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

Validate a cohort table input.
summary.cdm_reference

Summarise a cdm reference
validateWindowArgument

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

Identify settings columns of a <summarised_result>
validateCdmTable

Validate if a table is a valid cdm_table object.
summary.summarised_result

Summarise a summarised_result
splitAdditional

Split additional_name and additional_level columns
validateNameLevel

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

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

Get settings from a summarised_result object.
summarisedResultDoc

Helper for consistent documentation of summarised_result objects.
validateColumn

Validate whether a variable points to a certain existing column in a table.
validateCohortIdArgument

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

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

Status of the indexes
splitStrata

Split strata_name and strata_level columns
validationDoc

Helper for consistent documentation of validation mode.
supportedCdmVersions

Supported OMOP CDM versions
[[<-.cdm_reference

Assign a table to a cdm reference.
validateNewColumn

Validate a new column of a table
tidy.summarised_result

Turn a <summarised_result> object into a tidy tibble
tidyColumns

Identify tidy columns of a <summarised_result>
uniteAdditional

Unite one or more columns in additional_name-additional_level format
validateOmopTable

Validate an omop_table
validateResultArgument

Validate whether an object is a valid 'summarised_result' object.
validateStrataArgument

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

Create a unique table name
assertDoc

Helper for consistent documentation of assertion functions.
addSettings

Add settings columns to a <summarised_result> object
assertCharacter

Assert that an object is a character and satisfies certain conditions.
assertChoice

Assert that an object is one of a set of options.
achillesColumns

Required columns for each of the achilles result tables
assertClass

Assert that an object has a certain class.
achillesTables

Names of the tables that contain the results of achilles analyses
assertList

Assert that an object is a list.
additionalColumns

Identify variables in additional_name column
attrition

Get attrition from an object.
assertTrue

Assert that an expression is TRUE.
assertTable

Assert that an object is a table.
attrition.cohort_table

Get cohort attrition from a cohort_table object.
assertDate

Assert Date
bind.cohort_table

Bind two or more cohort tables
bind

Bind two or more objects of the same class.
assertLogical

Assert that an object is a logical.
cdmAssignTableDoc

Helper for consistent documentation of assigning tables to a CDM reference.
cdmClasses

Separate the cdm tables in classes
assertNumeric

Assert that an object is a numeric.