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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"): no date field in DESCRIPTION file of
#> package 'omopgenerics'
#> 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 0.3.1.900,
#>   <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 0.3.1.900},
#>     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(201820, 4087682, 3655269),
                        "asthma" = 317009)
condition_codes <- newCodelist(condition_codes)
#> Warning: ! `codelist` contains numeric values, they are casted to integers.

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

condition_cs
#> 
#> ── 2 conceptSetExpressions ─────────────────────────────────────────────────────
#> 
#> - 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 = 1, gender_concept_id = 0, year_of_birth = 1990,
  race_concept_id = 0, ethnicity_concept_id = 0
)
observation_period <- dplyr::tibble(
  observation_period_id = 1, person_id = 1,
  observation_period_start_date = as.Date("2000-01-01"),
  observation_period_end_date = as.Date("2023-12-31"),
  period_type_concept_id = 0
)
diabetes <- tibble(
  cohort_definition_id = 1, subject_id = 1,
  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"
)
#> Warning: ! 5 column in person do not match expected column type:
#> • `person_id` is numeric but expected integer
#> • `gender_concept_id` is numeric but expected integer
#> • `year_of_birth` is numeric but expected integer
#> • `race_concept_id` is numeric but expected integer
#> • `ethnicity_concept_id` is numeric but expected integer
#> Warning: ! 3 column in observation_period do not match expected column type:
#> • `observation_period_id` is numeric but expected integer
#> • `person_id` is numeric but expected integer
#> • `period_type_concept_id` is numeric but expected integer
cdm$diabetes <- newCohortTable(cdm$diabetes)
#> Warning: ! 2 column in diabetes do not match expected column type:
#> • `cohort_definition_id` is numeric but expected integer
#> • `subject_id` is numeric but expected integer

cdm$diabetes
#> # A tibble: 1 × 4
#>   cohort_definition_id subject_id cohort_start_date cohort_end_date
#>                  <dbl>      <dbl> <date>            <date>         
#> 1                    1          1 2020-01-01        2020-01-10
settings(cdm$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) |> 
  dplyr::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> "2024-11-01", "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) |> 
  dplyr::glimpse()
#> 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

2,269

Version

1.3.0

License

Apache License (>= 2)

Maintainer

Marti Catala

Last Published

July 15th, 2025

Functions in omopgenerics (1.3.0)

additionalColumns

Identify variables in additional_name column
assertTrue

Assert that an expression is TRUE.
cdmName

Get the name of a cdm_reference associated object
cdmFromTables

Create a cdm object from local tables
bind.cohort_table

Bind two or more cohort tables
attrition

Get attrition from an object.
bind.summarised_result

Bind two or summarised_result objects
$<-.cdm_reference

Assign an table to a cdm reference.
$.cdm_reference

Subset a cdm reference object.
assertNumeric

Assert that an object is a numeric.
cdmClasses

Separate the cdm tables in classes
assertTable

Assert that an object is a table.
attrition.cohort_table

Get cohort attrition from a cohort_table 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.
cdmDisconnect

Disconnect from a cdm object.
checkCohortRequirements

Check whether a cohort table satisfies requirements
cohortCodelist

Get codelist from a cohort_table object.
collect.cdm_reference

Retrieves the cdm reference into a local cdm.
cohortTables

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

Bind two or more objects of the same class.
cdmReference

Get the cdm_reference of a cdm_table.
cdmSource

Get the cdmSource of an object.
cdmSelect

Restrict the cdm object to a subset of tables.
cdmSourceType

Get the source type of a cdm_reference object.
createIndexes

Create the missing indexes
compute.cdm_table

Store results in a table.
emptyAchillesTable

Create an empty achilles table
emptyCdmReference

Create an empty cdm_reference
filterGroup

Filter the group_name-group_level pair in a summarised_result
dropSourceTable

Drop a table from a cdm object.
dropTable

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

Create an empty cohort_table object
cohortColumns

Required columns for a generated cohort set.
emptyOmopTable

Create an empty omop table
collect.cohort_table

To collect a cohort_table object.
combineStrata

Provide all combinations of strata levels.
filterSettings

Filter a <summarised_result> using the settings
existingIndexes

Existing indexes in a cdm object
groupColumns

Identify variables in group_name column
emptyCodelistWithDetails

Empty codelist object.
emptyCodelist

Empty codelist object.
cohortCount

Get cohort counts from a cohort_table object.
newCdmTable

Create an cdm table.
expectedIndexes

Expected indexes in a cdm object
importConceptSetExpression

Import a concept set expression.
importSummarisedResult

Import a set of summarised results.
newCodelist

'codelist' object constructor
exportCodelist

Export a codelist object.
exportConceptSetExpression

Export a concept set expression.
filterStrata

Filter the strata_name-strata_level pair in a summarised_result
createLogFile

Create a log file
estimateTypeChoices

Choices that can be present in estimate_type column.
emptySummarisedResult

Empty summarised_result object.
newCdmReference

cdm_reference objects constructor
createTableIndex

Create a table index
newConceptSetExpression

'conceptSetExpression' object constructor
newLocalSource

A new local source for the cdm
newCdmSource

Create a cdm source object.
logMessage

Log a message to a logFile
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.
newAchillesTable

Create an achilles table from a cdm_table.
insertCdmTo

Insert a cdm_reference object to a different source.
insertTable

Insert a table to a cdm object.
importCodelist

Import a codelist.
filterAdditional

Filter the additional_name-additional_level pair in a summarised_result
exportSummarisedResult

Export a summarised_result object to a csv file.
getCohortId

Get the cohort definition id of a certain name
omopTableFields

Return a table of omop cdm fields informations
omopColumns

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

'summarised_results' object constructor
isResultSuppressed

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

Create an omop table from a cdm table.
getCohortName

Get the cohort name of a certain cohort definition id
newCodelistWithDetails

'codelist' object constructor
newCohortTable

cohort_table objects constructor.
print.codelist

Print a codelist
print.codelist_with_details

Print a codelist with details
omopTables

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

Get settings from a summarised_result object.
numberSubjects

Count the number of subjects that a cdm_table has.
numberRecords

Count the number of records that a cdm_table has.
omopgenerics-package

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

Identify settings columns of a <summarised_result>
summary.summarised_result

Summary a summarised_result
summary.cohort_table

Summary a generated cohort set
splitAll

Split all pairs name-level into columns.
splitGroup

Split group_name and group_level columns
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.
tidyColumns

Identify tidy columns of a <summarised_result>
validateCdmArgument

Validate if an object in a valid cdm_reference.
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.
validateCdmTable

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

Turn a <summarised_result> object into a tidy tibble
suppress

Function to suppress counts in result objects
suppress.summarised_result

Function to suppress counts in result objects
uniteGroup

Unite one or more columns in group_name-group_level format
print.cdm_reference

Print a CDM reference object
pivotEstimates

Set estimates as columns
recordCohortAttrition

Update cohort attrition.
uniteStrata

Unite one or more columns in strata_name-strata_level format
isTableEmpty

Check if a table is empty or not
sourceType

Get the source type of an object.
summary.cdm_source

Summarise a cdm_source object
tmpPrefix

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

Split additional_name and additional_level columns
summary.cdm_reference

Summary a cdm reference
listSourceTables

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

Convert a character vector to snake case
validateNameLevel

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

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

Print a concept set expression
settings.cohort_table

Get cohort settings from a cohort_table object.
settings

Get settings from an object.
reexports

Objects exported from other packages
[[.cdm_reference

Subset a cdm reference object.
tableSource

Get the table source of a cdm_table.
strataColumns

Identify variables in strata_name column
tableName

Get the table name of a cdm_table.
splitStrata

Split strata_name and strata_level columns
validateCohortArgument

Validate a cohort table input.
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).
validateStrataArgument

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

Validate a new column of a table
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.
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.
validateAchillesTable

Validate if a cdm_table is a valid achilles table.
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.
statusIndexes

Status of the indexes
resultColumns

Required columns that the result tables must have.
validateOmopTable

Validate an omop_table
uniteAdditional

Unite one or more columns in additional_name-additional_level format
validateResultArgument

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

Create a unique table name
summariseLogFile

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

Check if different packages version are used for summarise_results object
[[<-.cdm_reference

Assign a table to a cdm reference.
transformToSummarisedResult

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

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

Validate conceptSet argument. It can either be a list, a codelist, a conceptSetExpression or a codelist with details. The output will always be a codelist.
validateColumn

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

Assert that an object is a logical.
addSettings

Add settings columns to a <summarised_result> object
assertClass

Assert that an object has a certain class.
achillesColumns

Required columns for each of the achilles result tables
assertDate

Assert Date
assertChoice

Assert that an object is within a certain oprtions.
achillesTables

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

Assert that an object is a list.
assertCharacter

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