Title:	Methods and Classes for the OMOP Common Data Model
Version:	1.3.0
Description:	Provides definitions of core classes and methods used by analytic pipelines that query the OMOP (Observational Medical Outcomes Partnership) common data model.
License:	Apache License (≥ 2)
Encoding:	UTF-8
RoxygenNote:	7.3.2
Imports:	cli, dbplyr, dplyr, generics, glue, lifecycle, methods, purrr, rlang, snakecase, stringi, stringr, tidyr, vctrs
Depends:	R (≥ 4.1)
Suggests:	bit64, covr, gt, here, jsonlite, knitr, readr, rmarkdown, testthat (≥ 3.0.0), tictoc, withr
URL:	https://darwin-eu.github.io/omopgenerics/
BugReports:	https://github.com/darwin-eu/omopgenerics/issues
Config/testthat/edition:	3
Config/testthat/parallel:	true
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-07-15 13:24:43 UTC; martics
Author:	Martí Català [aut, cre], Edward Burn [aut], Mike Du [ctb], Yuchen Guo [ctb], Adam Black [ctb], Marta Alcalde-Herraiz [ctb]
Maintainer:	Martí Català <marti.catalasabate@ndorms.ox.ac.uk>
Repository:	CRAN
Date/Publication:	2025-07-15 15:10:02 UTC

omopgenerics: Methods and Classes for the OMOP Common Data Model

Description

Provides definitions of core classes and methods used by analytic pipelines that query the OMOP (Observational Medical Outcomes Partnership) common data model.

Author(s)

Maintainer: Martí Català marti.catalasabate@ndorms.ox.ac.uk (ORCID)

Authors:

• Edward Burn edward.burn@ndorms.ox.ac.uk (ORCID)

Other contributors:

• Mike Du mike.du@ndorms.ox.ac.uk (ORCID) [contributor]

• Yuchen Guo yuchen.guo@ndorms.ox.ac.uk (ORCID) [contributor]

• Adam Black black@ohdsi.org (ORCID) [contributor]

• Marta Alcalde-Herraiz marta.alcaldeherraiz@ndorms.ox.ac.uk (ORCID) [contributor]

See Also

Useful links:

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

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

Assign an table to a cdm reference.

Description

Assign an table to a cdm reference.

Usage

## S3 replacement method for class 'cdm_reference'
cdm$name <- value

Arguments

Value

The cdm reference.

Examples

library(omopgenerics)

cdm <- cdmFromTables(
  tables = list(
    "person" = dplyr::tibble(
      person_id = c(1, 2, 3), gender_concept_id = 0, year_of_birth = 1990,
      race_concept_id = 0, ethnicity_concept_id = 0
    ),
    "observation_period" = dplyr::tibble(
      observation_period_id = 1:3, person_id = 1:3,
      observation_period_start_date = as.Date("2000-01-01"),
      observation_period_end_date = as.Date("2023-12-31"),
      period_type_concept_id = 0
    )
  ),
  cdmName = "mock"
)

cdm$person

Subset a cdm reference object.

Description

Subset a cdm reference object.

Usage

## S3 method for class 'cdm_reference'
x$name

Arguments

Value

A single cdm table reference

Examples

library(omopgenerics)
library(dplyr, warn.conflicts = FALSE)

cdm <- cdmFromTables(
  tables = list(
    "person" = tibble(
      person_id = c(1, 2, 3), gender_concept_id = 0, year_of_birth = 1990,
      race_concept_id = 0, ethnicity_concept_id = 0
    ),
    "observation_period" = tibble(
      observation_period_id = 1:3, person_id = 1:3,
      observation_period_start_date = as.Date("2000-01-01"),
      observation_period_end_date = as.Date("2023-12-31"),
      period_type_concept_id = 0
    )
  ),
  cdmName = "mock"
)

cdm$person

Assign a table to a cdm reference.

Description

Assign a table to a cdm reference.

Usage

## S3 replacement method for class 'cdm_reference'
cdm[[name]] <- value

Arguments

Value

The cdm reference.

Subset a cdm reference object.

Description

Subset a cdm reference object.

Usage

## S3 method for class 'cdm_reference'
x[[name]]

Arguments

Value

A single cdm table reference

Examples

library(omopgenerics)
library(dplyr, warn.conflicts = FALSE)

cdm <- cdmFromTables(
  tables = list(
    "person" = tibble(
      person_id = c(1, 2, 3), gender_concept_id = 0, year_of_birth = 1990,
      race_concept_id = 0, ethnicity_concept_id = 0
    ),
    "observation_period" = tibble(
      observation_period_id = 1:3, person_id = 1:3,
      observation_period_start_date = as.Date("2000-01-01"),
      observation_period_end_date = as.Date("2023-12-31"),
      period_type_concept_id = 0
    )
  ),
  cdmName = "mock"
)

cdm[["person"]]

Required columns for each of the achilles result tables

Description

Required columns for each of the achilles result tables

Usage

achillesColumns(table, version = "5.3", onlyRequired = lifecycle::deprecated())

Arguments

Value

Character vector with the column names

Examples

library(omopgenerics)
achillesColumns("achilles_analysis")
achillesColumns("achilles_results")
achillesColumns("achilles_results_dist")

Names of the tables that contain the results of achilles analyses

Description

Names of the tables that contain the results of achilles analyses

Usage

achillesTables(version = "5.3")

Arguments

Value

Names of the tables that are contain the results from the achilles analyses

Examples

library(omopgenerics)
achillesTables()

Add settings columns to a ⁠<summarised_result>⁠ object

Description

Add settings columns to a ⁠<summarised_result>⁠ object

Usage

addSettings(result, settingsColumn = settingsColumns(result))

Arguments

Value

A ⁠<summarised_result>⁠ object with the added setting columns.

Examples

{
  library(dplyr)
  library(omopgenerics)

  x <- tibble(
    "result_id" = as.integer(c(1, 2)),
    "cdm_name" = c("cprd", "eunomia"),
    "group_name" = "cohort_name",
    "group_level" = "my_cohort",
    "strata_name" = "sex",
    "strata_level" = "male",
    "variable_name" = "Age group",
    "variable_level" = "10 to 50",
    "estimate_name" = "count",
    "estimate_type" = "numeric",
    "estimate_value" = "5",
    "additional_name" = "overall",
    "additional_level" = "overall"
  ) |>
    newSummarisedResult(settings = tibble(
      "result_id" = c(1, 2), "custom" = c("A", "B")
    ))

  x

  x |> addSettings()
}

Identify variables in additional_name column

Description

Identifies and returns the unique values in additional_name column.

Usage

additionalColumns(result)

Arguments

Value

Unique values of the additional name column.

Examples

{
  library(dplyr)
  library(omopgenerics)

  x <- tibble(
    "result_id" = as.integer(c(1, 2)),
    "cdm_name" = c("cprd", "eunomia"),
    "group_name" = "cohort_name",
    "group_level" = "my_cohort",
    "strata_name" = "sex",
    "strata_level" = "male",
    "variable_name" = "Age group",
    "variable_level" = "10 to 50",
    "estimate_name" = "count",
    "estimate_type" = "numeric",
    "estimate_value" = "5",
    "additional_name" = "overall",
    "additional_level" = "overall"
  ) |>
    newSummarisedResult(settings = tibble(
      "result_id" = c(1, 2), "custom" = c("A", "B")
    ))

  x

  x |> additionalColumns()
}

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

Description

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

Usage

assertCharacter(
  x,
  length = NULL,
  na = FALSE,
  null = FALSE,
  unique = FALSE,
  named = FALSE,
  minNumCharacter = 0,
  call = parent.frame(),
  msg = NULL
)

Arguments

Assert that an object is within a certain oprtions.

Description

Assert that an object is within a certain oprtions.

Usage

assertChoice(
  x,
  choices,
  length = NULL,
  na = FALSE,
  null = FALSE,
  unique = FALSE,
  named = FALSE,
  call = parent.frame(),
  msg = NULL
)

Arguments

Assert that an object has a certain class.

Description

Assert that an object has a certain class.

Usage

assertClass(
  x,
  class,
  length = NULL,
  null = FALSE,
  all = FALSE,
  extra = TRUE,
  call = parent.frame(),
  msg = NULL
)

Arguments

Assert Date

Description

Assert Date

Usage

assertDate(
  x,
  length = NULL,
  na = FALSE,
  null = FALSE,
  unique = FALSE,
  named = FALSE,
  call = parent.frame(),
  msg = NULL
)

Arguments

Value

x

Assert that an object is a list.

Description

Assert that an object is a list.

Usage

assertList(
  x,
  length = NULL,
  na = FALSE,
  null = FALSE,
  unique = FALSE,
  named = FALSE,
  class = NULL,
  call = parent.frame(),
  msg = NULL
)

Arguments

Assert that an object is a logical.

Description

Assert that an object is a logical.

Usage

assertLogical(
  x,
  length = NULL,
  na = FALSE,
  null = FALSE,
  named = FALSE,
  call = parent.frame(),
  msg = NULL
)

Arguments

Assert that an object is a numeric.

Description

Assert that an object is a numeric.

Usage

assertNumeric(
  x,
  integerish = FALSE,
  min = -Inf,
  max = Inf,
  length = NULL,
  na = FALSE,
  null = FALSE,
  unique = FALSE,
  named = FALSE,
  call = parent.frame(),
  msg = NULL
)

Arguments

Assert that an object is a table.

Description

Assert that an object is a table.

Usage

assertTable(
  x,
  class = NULL,
  numberColumns = NULL,
  numberRows = NULL,
  columns = character(),
  allowExtraColumns = TRUE,
  null = FALSE,
  unique = FALSE,
  call = parent.frame(),
  msg = NULL
)

Arguments

Assert that an expression is TRUE.

Description

Assert that an expression is TRUE.

Usage

assertTrue(x, null = FALSE, call = parent.frame(), msg = NULL)

Arguments

Get attrition from an object.

Description

Get attrition from an object.

Usage

attrition(x)

Arguments

Value

A table with the attrition.

Get cohort attrition from a cohort_table object.

Description

Get cohort attrition from a cohort_table object.

Usage

## S3 method for class 'cohort_table'
attrition(x)

Arguments

Value

A table with the attrition.

Examples

 
library(omopgenerics)
library(dplyr, warn.conflicts = FALSE)

person <- tibble(
  person_id = 1, gender_concept_id = 0, year_of_birth = 1990,
  race_concept_id = 0, ethnicity_concept_id = 0
)
observation_period <- 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
)
cohort <- tibble(
  cohort_definition_id = c(1, 1, 1, 2),
  subject_id = 1,
  cohort_start_date = as.Date(c("2020-01-01", "2021-01-01", "2022-01-01", "2022-01-01")),
  cohort_end_date = as.Date(c("2020-01-01", "2021-01-01", "2022-01-01", "2022-01-01")),
)
cdm <- cdmFromTables(
  tables = list("person" = person, "observation_period" = observation_period),
  cdmName = "my_example_cdm",
  cohortTables = list("cohort1" = cohort)
)

attrition(cdm$cohort1)

Bind two or more objects of the same class.

Description

Bind two or more objects of the same class.

Usage

bind(...)

Arguments

Value

New object.

Bind two or more cohort tables

Description

Bind two or more cohort tables

Usage

## S3 method for class 'cohort_table'
bind(..., name)

Arguments

Value

The cdm object with a new generated cohort set containing all of the cohorts passed.

Examples

library(omopgenerics)
library(dplyr, warn.conflicts = FALSE)

cohort1 <- tibble(
  cohort_definition_id = 1,
  subject_id = 1:3,
  cohort_start_date = as.Date("2010-01-01"),
  cohort_end_date = as.Date("2010-01-05")
)
cohort2 <- tibble(
  cohort_definition_id = c(2, 2, 3, 3, 3),
  subject_id = c(1, 2, 3, 1, 2),
  cohort_start_date = as.Date("2010-01-01"),
  cohort_end_date = as.Date("2010-01-05")
)
cdm <- cdmFromTables(
  tables = list(
    "person" = tibble(
      person_id = c(1, 2, 3), gender_concept_id = 0, year_of_birth = 1990,
      race_concept_id = 0, ethnicity_concept_id = 0
    ),
    "observation_period" = tibble(
      observation_period_id = 1:3, person_id = 1:3,
      observation_period_start_date = as.Date("2000-01-01"),
      observation_period_end_date = as.Date("2023-12-31"),
      period_type_concept_id = 0
    )
  ),
  cdmName = "mock",
  cohortTables = list("cohort1" = cohort1, "cohort2" = cohort2)
)

cdm <- bind(cdm$cohort1, cdm$cohort2, name = "cohort3")
settings(cdm$cohort3)
cdm$cohort3

Bind two or summarised_result objects

Description

Bind two or summarised_result objects

Usage

## S3 method for class 'summarised_result'
bind(...)

Arguments

Value

A summarised_result object the merged objects.

Examples

library(omopgenerics)
library(dplyr, warn.conflicts = FALSE)

cdm <- cdmFromTables(
  tables = list(
    "person" = tibble(
      person_id = c(1, 2, 3), gender_concept_id = 0, year_of_birth = 1990,
      race_concept_id = 0, ethnicity_concept_id = 0
    ),
    "observation_period" = tibble(
      observation_period_id = 1:3, person_id = 1:3,
      observation_period_start_date = as.Date("2000-01-01"),
      observation_period_end_date = as.Date("2023-12-31"),
      period_type_concept_id = 0
    )
  ),
  cdmName = "mock",
  cohortTables = list("cohort1" = tibble(
    cohort_definition_id = 1,
    subject_id = 1:3,
    cohort_start_date = as.Date("2010-01-01"),
    cohort_end_date = as.Date("2010-01-05")
  ))
)

result1 <- summary(cdm)
result2 <- summary(cdm$cohort1)

mergedResult <- bind(result1, result2)
mergedResult

Separate the cdm tables in classes

Description

Separate the cdm tables in classes

Usage

cdmClasses(cdm)

Arguments

Value

A list of table names, the name of the list indicates the class.

Disconnect from a cdm object.

Description

Disconnect from a cdm object.

Usage

cdmDisconnect(cdm, ...)

Arguments

Value

TRUE if process wass successful. library(omopgenerics) library(dplyr, warn.conflicts = FALSE)

person <- tibble( person_id = 1, gender_concept_id = 0, year_of_birth = 1990, race_concept_id = 0, ethnicity_concept_id = 0 ) observation_period <- 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 ) cdm <- cdmFromTables( tables = list("person" = person, "observation_period" = observation_period), cdmName = "mock" )

cdmDisconnect(cdm)

Create a cdm object from local tables

Description

Create a cdm object from local tables

Usage

cdmFromTables(tables, cdmName, cohortTables = list(), cdmVersion = NULL)

Arguments

Value

A cdm_reference object.

Examples

library(omopgenerics)
library(dplyr, warn.conflicts = FALSE)

person <- tibble(
  person_id = 1, gender_concept_id = 0, year_of_birth = 1990,
  race_concept_id = 0, ethnicity_concept_id = 0
)
observation_period <- 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
)
cdm <- cdmFromTables(
  tables = list("person" = person, "observation_period" = observation_period),
  cdmName = "test"
)

Get the name of a cdm_reference associated object

Description

Get the name of a cdm_reference associated object

Usage

cdmName(x)

Arguments

Value

Name of the cdm_reference.

Examples

library(omopgenerics)
library(dplyr, warn.conflicts = FALSE)

cdm <- cdmFromTables(
  tables = list(
    "person" = tibble(
      person_id = c(1, 2, 3), gender_concept_id = 0, year_of_birth = 1990,
      race_concept_id = 0, ethnicity_concept_id = 0
    ),
    "observation_period" = tibble(
      observation_period_id = 1:3, person_id = 1:3,
      observation_period_start_date = as.Date("2000-01-01"),
      observation_period_end_date = as.Date("2023-12-31"),
      period_type_concept_id = 0
    )
  ),
  cdmName = "mock"
)

cdmName(cdm)

cdmName(cdm$person)

Get the cdm_reference of a cdm_table.

Description

Get the cdm_reference of a cdm_table.

Usage

cdmReference(table)

Arguments

Value

A cdm_reference.

Examples

library(omopgenerics)
library(dplyr, warn.conflicts = FALSE)

cdm <- cdmFromTables(
  tables = list(
    "person" = tibble(
      person_id = c(1, 2, 3), gender_concept_id = 0, year_of_birth = 1990,
      race_concept_id = 0, ethnicity_concept_id = 0
    ),
    "observation_period" = tibble(
      observation_period_id = 1:3, person_id = 1:3,
      observation_period_start_date = as.Date("2000-01-01"),
      observation_period_end_date = as.Date("2023-12-31"),
      period_type_concept_id = 0
    )
  ),
  cdmName = "mock"
)

cdmReference(cdm$person)

Restrict the cdm object to a subset of tables.

Description

Restrict the cdm object to a subset of tables.

Usage

cdmSelect(cdm, ...)

Arguments

Value

A cdm_reference with only the specified tables.

Examples

cdm <- emptyCdmReference("my cdm")
cdm

cdm |>
  cdmSelect("person")

Get the cdmSource of an object.

Description

Get the cdmSource of an object.

Usage

cdmSource(x, cdm = lifecycle::deprecated())

Arguments

Value

A cdm_source object.

Examples

library(omopgenerics)
library(dplyr, warn.conflicts = FALSE)

cdm <- cdmFromTables(
  tables = list(
    "person" = tibble(
      person_id = c(1, 2, 3), gender_concept_id = 0, year_of_birth = 1990,
      race_concept_id = 0, ethnicity_concept_id = 0
    ),
    "observation_period" = tibble(
      observation_period_id = 1:3, person_id = 1:3,
      observation_period_start_date = as.Date("2000-01-01"),
      observation_period_end_date = as.Date("2023-12-31"),
      period_type_concept_id = 0
    )
  ),
  cdmName = "mock"
)

cdmSource(cdm)
cdmSource(cdm$person)

Get the source type of a cdm_reference object.

Description

Usage

cdmSourceType(cdm)

Arguments

Value

A character vector with the type of source of the cdm_reference object.

Examples

library(omopgenerics)
library(dplyr, warn.conflicts = FALSE)

cdm <- cdmFromTables(
  tables = list(
    "person" = tibble(
      person_id = c(1, 2, 3), gender_concept_id = 0, year_of_birth = 1990,
      race_concept_id = 0, ethnicity_concept_id = 0
    ),
    "observation_period" = tibble(
      observation_period_id = 1:3, person_id = 1:3,
      observation_period_start_date = as.Date("2000-01-01"),
      observation_period_end_date = as.Date("2023-12-31"),
      period_type_concept_id = 0
    )
  ),
  cdmName = "mock"
)

cdmSourceType(cdm)

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.

Description

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.

Usage

cdmTableFromSource(src, value)

Arguments

Value

A cdm_table.

Get the version of an object.

Description

Get the version of an object.

Usage

cdmVersion(x)

Arguments

Value

A character vector indicating the cdm version.

Examples

library(omopgenerics)
library(dplyr, warn.conflicts = FALSE)

cdm <- cdmFromTables(
  tables = list(
    "person" = tibble(
      person_id = c(1, 2, 3), gender_concept_id = 0, year_of_birth = 1990,
      race_concept_id = 0, ethnicity_concept_id = 0
    ),
    "observation_period" = tibble(
      observation_period_id = 1:3, person_id = 1:3,
      observation_period_start_date = as.Date("2000-01-01"),
      observation_period_end_date = as.Date("2023-12-31"),
      period_type_concept_id = 0
    )
  ),
  cdmName = "mock"
)

cdmVersion(cdm)
cdmVersion(cdm$person)

Check whether a cohort table satisfies requirements

Description

Usage

checkCohortRequirements(
  cohort,
  checkEndAfterStart = TRUE,
  checkOverlappingEntries = TRUE,
  checkMissingValues = TRUE,
  checkInObservation = TRUE,
  type = "error",
  call = parent.frame()
)

Arguments

Value

An error will be returned if any of the selected checks fail.

Get codelist from a cohort_table object.

Description

Get codelist from a cohort_table object.

Usage

cohortCodelist(
  cohortTable,
  cohortId,
  codelistType = c("index event", "inclusion criteria", "exit criteria"),
  type = lifecycle::deprecated()
)

Arguments

Value

A table with the codelists used.

Examples

 
library(omopgenerics)
library(dplyr, warn.conflicts = FALSE)

person <- tibble(
  person_id = 1, gender_concept_id = 0, year_of_birth = 1990,
  race_concept_id = 0, ethnicity_concept_id = 0
)
observation_period <- 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
)
cohort <- tibble(
  cohort_definition_id = c(1, 1, 1, 2),
  subject_id = 1,
  cohort_start_date = as.Date(c(
    "2020-01-01", "2021-01-01", "2022-01-01", "2022-01-01"
  )),
  cohort_end_date = as.Date(c(
    "2020-01-01", "2021-01-01", "2022-01-01", "2022-01-01"
  ))
)
cdm <- cdmFromTables(
  tables = list("person" = person, "observation_period" = observation_period),
  cdmName = "my_example_cdm",
  cohortTables = list("cohort1" = cohort)
)
cdm$cohort1 <- newCohortTable(table = cdm$cohort1,
                                cohortCodelistRef = dplyr::tibble(
                                cohort_definition_id = c(1,1,1,2,2),
                                codelist_name =c("disease X", "disease X", "disease X",
                                                 "disease Y", "disease Y"),
                                concept_id = c(1,2,3,4,5),
                                codelist_type = "index event"
                              ))
cohortCodelist(cdm$cohort1, cohortId = 1, codelistType = "index event")

Required columns for a generated cohort set.

Description

Required columns for a generated cohort set.

Usage

cohortColumns(table, version = "5.3")

Arguments

Value

Character vector with the column names

Required columns

Examples

library(omopgenerics)
cohortColumns("cohort")

Get cohort counts from a cohort_table object.

Description

Get cohort counts from a cohort_table object.

Usage

cohortCount(cohort)

Arguments

Value

A table with the counts.

Examples

 
library(omopgenerics)
library(dplyr, warn.conflicts = FALSE)

person <- tibble(
  person_id = 1, gender_concept_id = 0, year_of_birth = 1990,
  race_concept_id = 0, ethnicity_concept_id = 0
)
observation_period <- 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
)
cohort <- tibble(
  cohort_definition_id = c(1, 1, 1, 2),
  subject_id = 1,
  cohort_start_date = as.Date(c(
    "2020-01-01", "2021-01-01", "2022-01-01", "2022-01-01"
  )),
  cohort_end_date = as.Date(c(
    "2020-01-01", "2021-01-01", "2022-01-01", "2022-01-01"
  )),
)
cdm <- cdmFromTables(
  tables = list("person" = person, "observation_period" = observation_period),
  cdmName = "my_example_cdm",
  cohortTables = list("cohort1" = cohort)
)

cohortCount(cdm$cohort1)

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

Description

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

Usage

cohortTables(version = "5.3")

Arguments

Value

cohort tables

Examples

library(omopgenerics)
cohortTables()

Retrieves the cdm reference into a local cdm.

Description

Retrieves the cdm reference into a local cdm.

Usage

## S3 method for class 'cdm_reference'
collect(x, ...)

Arguments

Value

A local cdm_reference.

Examples

library(omopgenerics)
library(dplyr, warn.conflicts = FALSE)

cdm <- cdmFromTables(
  tables = list(
    "person" = dplyr::tibble(
      person_id = c(1, 2, 3), gender_concept_id = 0, year_of_birth = 1990,
      race_concept_id = 0, ethnicity_concept_id = 0
    ),
    "observation_period" = dplyr::tibble(
      observation_period_id = 1:3, person_id = 1:3,
      observation_period_start_date = as.Date("2000-01-01"),
      observation_period_end_date = as.Date("2023-12-31"),
      period_type_concept_id = 0
    )
  ),
  cdmName = "mock"
)

collect(cdm)

To collect a cohort_table object.

Description

To collect a cohort_table object.

Usage

## S3 method for class 'cohort_table'
collect(x, ...)

Arguments

Value

A data frame with the cohort_table

Provide all combinations of strata levels.

Description

Provide all combinations of strata levels.

Usage

combineStrata(levels, overall = FALSE)

Arguments

Value

A vector of all combinations of strata.

Examples

combineStrata(character())
combineStrata(character(), overall = TRUE)
combineStrata(c("age", "sex"), overall = TRUE)
combineStrata(c("age", "sex", "year"))

Store results in a table.

Description

Store results in a table.

Usage

## S3 method for class 'cdm_table'
compute(
  x,
  name = NULL,
  temporary = NULL,
  overwrite = TRUE,
  logPrefix = NULL,
  ...
)

Arguments

Value

Reference to a table in the cdm

Create the missing indexes

Description

Usage

createIndexes(cdm, name = NULL)

Arguments

Value

Whether the process was completed successfully.

Create a log file

Description

Create a log file

Usage

createLogFile(logFile = here::here("log_{date}_{time}"))

Arguments

Value

Invisible TRUE if logger was created correctly.

Examples

library(dplyr)

logFile <- tempfile(pattern = "log_{date}_{time}", fileext = ".txt")
createLogFile(logFile = logFile)

logMessage("Starting analysis")
1 + 1
logMessage("Analysis finished")

res <- summariseLogFile()

glimpse(res)

tidy(res)

Create a table index

Description

Usage

createTableIndex(table, index)

Arguments

Value

Whether the index could be created

Drop a table from a cdm object.

Description

Drop a table from a cdm object.

Usage

dropSourceTable(cdm, name)

Arguments

Value

The table in the cdm reference.

Drop a table from a cdm object.

Description

Drop a table from a cdm object.

Usage

dropTable(cdm, name)

Arguments

Value

The cdm reference.

Create an empty achilles table

Description

Create an empty achilles table

Usage

emptyAchillesTable(cdm, name)

Arguments

Value

The cdm_reference with an achilles empty table

Examples

library(omopgenerics)
cdm <- emptyCdmReference("my_example_cdm")
emptyAchillesTable(cdm = cdm, name = "achilles_results")

Create an empty cdm_reference

Description

Create an empty cdm_reference

Usage

emptyCdmReference(cdmName, cdmVersion = NULL)

Arguments

Value

An empty cdm_reference

Examples

library(omopgenerics)
emptyCdmReference(cdmName = "my_example_cdm")

Empty codelist object.

Description

Empty codelist object.

Usage

emptyCodelist()

Value

An empty codelist object.

Examples

emptyCodelist()

Empty codelist object.

Description

Empty codelist object.

Usage

emptyCodelistWithDetails()

Value

An empty codelist object.

Examples

emptyCodelistWithDetails()

Create an empty cohort_table object

Description

Create an empty cohort_table object

Usage

emptyCohortTable(cdm, name, overwrite = TRUE)

Arguments

Value

The cdm_reference with an empty cohort table

Examples

library(omopgenerics)
library(dplyr, warn.conflicts = FALSE)

person <- tibble(
  person_id = 1, gender_concept_id = 0, year_of_birth = 1990,
  race_concept_id = 0, ethnicity_concept_id = 0
)
observation_period <- 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
)
cdm <- cdmFromTables(
  tables = list("person" = person, "observation_period" = observation_period),
  cdmName = "test"
)

cdm <- emptyCohortTable(cdm, "my_empty_cohort")

cdm
cdm$my_empty_cohort
settings(cdm$my_empty_cohort)
attrition(cdm$my_empty_cohort)
cohortCount(cdm$my_empty_cohort)

Create an empty omop table

Description

Create an empty omop table

Usage

emptyOmopTable(cdm, name)

Arguments

Value

The cdm_reference with an empty cohort table

Examples

library(omopgenerics)

person <- dplyr::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
)
cdm <- cdmFromTables(
  tables = list("person" = person, "observation_period" = observation_period),
  cdmName = "test"
)

cdm <- emptyOmopTable(cdm, "drug_exposure")

cdm$drug_exposure

Empty summarised_result object.

Description

Empty summarised_result object.

Usage

emptySummarisedResult(settings = NULL)

Arguments

Value

An empty summarised_result object.

Examples

library(omopgenerics)

emptySummarisedResult()

Choices that can be present in estimate_type column.

Description

Choices that can be present in estimate_type column.

Usage

estimateTypeChoices()

Value

A character vector with the options that can be present in estimate_type column in the summarised_result objects.

Examples

library(omopgenerics)

estimateTypeChoices()

Existing indexes in a cdm object

Description

Usage

existingIndexes(cdm, name)

Arguments

Value

A tibble with 3 columns: table_class class of the table, table_name name of the table, and existing_index index definition.

Expected indexes in a cdm object

Description

Usage

expectedIndexes(cdm, name)

Arguments

Value

A tibble with 3 columns: table_class class of the table, table_name name of the table, and expected_index index definition.

Export a codelist object.

Description

Export a codelist object.

Usage

exportCodelist(x, path, type = "json")

Arguments

Value

Files with codelists

Export a concept set expression.

Description

Export a concept set expression.

Usage

exportConceptSetExpression(x, path, type = "json")

Arguments

Value

Files with concept set expressions

Export a summarised_result object to a csv file.

Description

Export a summarised_result object to a csv file.

Usage

exportSummarisedResult(
  ...,
  minCellCount = 5,
  fileName = "results_{cdm_name}_{date}.csv",
  path = getwd(),
  logFile = getOption("omopgenerics.logFile")
)

Arguments

Filter the additional_name-additional_level pair in a summarised_result

Description

Filter the additional_name-additional_level pair in a summarised_result

Usage

filterAdditional(result, ...)

Arguments

Value

A ⁠<summarised_result>⁠ object with only the rows that fulfill the required specified additional.

Examples

library(dplyr)
library(omopgenerics)

x <- tibble(
  "result_id" = 1L,
  "cdm_name" = "eunomia",
  "group_name" = "cohort_name",
  "group_level" = c("cohort1", "cohort2", "cohort3"),
  "strata_name" = "sex",
  "strata_level" = "Female",
  "variable_name" = "number subjects",
  "variable_level" = NA_character_,
  "estimate_name" = "count",
  "estimate_type" = "integer",
  "estimate_value" = c("100", "44", "14"),
  "additional_name" = c("year", "time_step", "year &&& time_step"),
  "additional_level" = c("2010", "4", "2015 &&& 5")
) |>
  newSummarisedResult()

x |>
  filterAdditional(year == "2010")

Filter the group_name-group_level pair in a summarised_result

Description

Filter the group_name-group_level pair in a summarised_result

Usage

filterGroup(result, ...)

Arguments

Value

A ⁠<summarised_result>⁠ object with only the rows that fulfill the required specified group.

Examples

library(dplyr)
library(omopgenerics)

x <- tibble(
  "result_id" = 1L,
  "cdm_name" = "eunomia",
  "group_name" = c("cohort_name", "age_group &&& cohort_name", "age_group"),
  "group_level" = c("my_cohort", ">40 &&& second_cohort", "<40"),
  "strata_name" = "sex",
  "strata_level" = "Female",
  "variable_name" = "number subjects",
  "variable_level" = NA_character_,
  "estimate_name" = "count",
  "estimate_type" = "integer",
  "estimate_value" = c("100", "44", "14"),
  "additional_name" = "overall",
  "additional_level" = "overall"
) |>
  newSummarisedResult()

x |>
  filterGroup(cohort_name == "second_cohort")

Filter a ⁠<summarised_result>⁠ using the settings

Description

Filter a ⁠<summarised_result>⁠ using the settings

Usage

filterSettings(result, ...)

Arguments

Value

A ⁠<summarised_result>⁠ object with only the result_id rows that fulfill the required specified settings.

Examples

library(dplyr)
library(omopgenerics)

x <- tibble(
  "result_id" = as.integer(c(1, 2)),
  "cdm_name" = c("cprd", "eunomia"),
  "group_name" = "cohort_name",
  "group_level" = "my_cohort",
  "strata_name" = "sex",
  "strata_level" = "male",
  "variable_name" = "Age group",
  "variable_level" = "10 to 50",
  "estimate_name" = "count",
  "estimate_type" = "numeric",
  "estimate_value" = "5",
  "additional_name" = "overall",
  "additional_level" = "overall"
) |>
  newSummarisedResult(settings = tibble(
    "result_id" = c(1, 2), "custom" = c("A", "B")
  ))

x

x |> filterSettings(custom == "A")

Filter the strata_name-strata_level pair in a summarised_result

Description

Filter the strata_name-strata_level pair in a summarised_result

Usage

filterStrata(result, ...)

Arguments

Value

A ⁠<summarised_result>⁠ object with only the rows that fulfill the required specified strata.

Examples

library(dplyr)
library(omopgenerics)

x <- tibble(
  "result_id" = 1L,
  "cdm_name" = "eunomia",
  "group_name" = "cohort_name",
  "group_level" = "my_cohort",
  "strata_name" = c("sex", "sex &&& age_group", "sex &&& year"),
  "strata_level" = c("Female", "Male &&& <40", "Female &&& 2010"),
  "variable_name" = "number subjects",
  "variable_level" = NA_character_,
  "estimate_name" = "count",
  "estimate_type" = "integer",
  "estimate_value" = c("100", "44", "14"),
  "additional_name" = "overall",
  "additional_level" = "overall"
) |>
  newSummarisedResult()

x |>
  filterStrata(sex == "Female")

Get the cohort definition id of a certain name

Description

Get the cohort definition id of a certain name

Usage

getCohortId(cohort, cohortName = NULL)

Arguments

Value

Cohort definition ids

Get the cohort name of a certain cohort definition id

Description

Get the cohort name of a certain cohort definition id

Usage

getCohortName(cohort, cohortId = NULL)

Arguments

Value

Cohort names

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.

Description

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.

Usage

getPersonIdentifier(x, call = parent.frame())

Arguments

Value

Person identifier column.

Identify variables in group_name column

Description

Identifies and returns the unique values in group_name column.

Usage

groupColumns(result)

Arguments

Value

Unique values of the group name column.

Examples

{
  library(dplyr)
  library(omopgenerics)

  x <- tibble(
    "result_id" = as.integer(c(1, 2)),
    "cdm_name" = c("cprd", "eunomia"),
    "group_name" = "cohort",
    "group_level" = "my_cohort",
    "strata_name" = "sex",
    "strata_level" = "male",
    "variable_name" = "Age group",
    "variable_level" = "10 to 50",
    "estimate_name" = "count",
    "estimate_type" = "numeric",
    "estimate_value" = "5",
    "additional_name" = "overall",
    "additional_level" = "overall"
  ) |>
    newSummarisedResult(settings = tibble(
      "result_id" = c(1, 2), "custom" = c("A", "B")
    ))

  x

  x |> groupColumns()
}

Import a codelist.

Description

Import a codelist.

Usage

importCodelist(path, type = "json")

Arguments

Value

A codelist

Import a concept set expression.

Description

Import a concept set expression.

Usage

importConceptSetExpression(path, type = "json")

Arguments

Value

A concept set expression

Import a set of summarised results.

Description

Import a set of summarised results.

Usage

importSummarisedResult(path, recursive = FALSE, ...)

Arguments

Value

A summarised result

Insert a cdm_reference object to a different source.

Description

Insert a cdm_reference object to a different source.

Usage

insertCdmTo(cdm, to)

Arguments

Value

The first cdm_reference object inserted to the source.

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.

Description

Usage

insertFromSource(cdm, value)

Arguments

Value

A table in the cdm_reference environment

Insert a table to a cdm object.

Description

Insert a table to a cdm object.

Usage

insertTable(cdm, name, table, overwrite = TRUE, temporary = FALSE)

Arguments

Value

The cdm reference. library(omopgenerics) library(dplyr, warn.conflicts = FALSE)

person <- tibble( person_id = 1, gender_concept_id = 0, year_of_birth = 1990, race_concept_id = 0, ethnicity_concept_id = 0 ) observation_period <- 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 ) cdm <- cdmFromTables( tables = list("person" = person, "observation_period" = observation_period), cdmName = "my_example_cdm" )

x <- tibble(a = 1)

cdm <- insertTable(cdm = cdm, name = "new_table", table = x)

cdm$new_table

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

Description

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

Usage

isResultSuppressed(result, minCellCount = 5)

Arguments

Value

Warning or message with check result

Examples

x <- dplyr::tibble(
  "result_id" = 1L,
  "cdm_name" = "eunomia",
  "group_name" = "cohort_name",
  "group_level" = "my_cohort",
  "strata_name" = c("sex", "sex &&& age_group", "sex &&& year"),
  "strata_level" = c("Female", "Male &&& <40", "Female &&& 2010"),
  "variable_name" = "number subjects",
  "variable_level" = NA_character_,
  "estimate_name" = "count",
  "estimate_type" = "integer",
  "estimate_value" = c("100", "44", "14"),
  "additional_name" = "overall",
  "additional_level" = "overall"
) |>
  newSummarisedResult()

isResultSuppressed(x)

Check if a table is empty or not

Description

Check if a table is empty or not

Usage

isTableEmpty(table)

Arguments

Value

Boolean to indicate if a cdm_table is empty (TRUE or FALSE).

List tables that can be accessed though a cdm object.

Description

List tables that can be accessed though a cdm object.

Usage

listSourceTables(cdm)

Arguments

Value

A character vector with the names of tables.

Log a message to a logFile

Description

The message is written to the logFile and displayed in the console, if logFile does not exist the message is only displayed in the console.

Usage

logMessage(
  message = "Start logging file",
  logFile = getOption("omopgenerics.logFile")
)

Arguments

Value

Invisible TRUE if the logging message is written to a log file.

Examples

library(dplyr)

logFile <- tempfile(pattern = "log_{date}_{time}", fileext = ".txt")
createLogFile(logFile = logFile)

logMessage("Starting analysis")
1 + 1
logMessage("Analysis finished")

res <- summariseLogFile()

glimpse(res)

tidy(res)

Create an achilles table from a cdm_table.

Description

Create an achilles table from a cdm_table.

Usage

newAchillesTable(table, version = "5.3", cast = FALSE)

Arguments

Value

An achilles_table object

cdm_reference objects constructor

Description

cdm_reference objects constructor

Usage

newCdmReference(tables, cdmName, cdmVersion = NULL, .softValidation = FALSE)

Arguments

Value

A cdm_reference object.

Examples

library(omopgenerics)
library(dplyr, warn.conflicts = FALSE)

cdmTables <- list(
  "person" = tibble(
    person_id = 1, gender_concept_id = 0, year_of_birth = 1990,
    race_concept_id = 0, ethnicity_concept_id = 0
  ) |>
    newCdmTable(newLocalSource(), "person"),
  "observation_period" = 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
  ) |>
    newCdmTable(newLocalSource(), "observation_period")
)
cdm <- newCdmReference(tables = cdmTables, cdmName = "mock")

cdm

Create a cdm source object.

Description

Create a cdm source object.

Usage

newCdmSource(src, sourceType)

Arguments

Value

A validated cdm source object.

Create an cdm table.

Description

Create an cdm table.

Usage

newCdmTable(table, src, name)

Arguments

Value

A cdm_table object

'codelist' object constructor

Description

'codelist' object constructor

Usage

newCodelist(x)

Arguments

Value

A codelist object.

'codelist' object constructor

Description

'codelist' object constructor

Usage

newCodelistWithDetails(x)

Arguments

Value

A codelist object.

cohort_table objects constructor.

Description

cohort_table objects constructor.

Usage

newCohortTable(
  table,
  cohortSetRef = attr(table, "cohort_set"),
  cohortAttritionRef = attr(table, "cohort_attrition"),
  cohortCodelistRef = attr(table, "cohort_codelist"),
  .softValidation = FALSE
)

Arguments

Value

A cohort_table object

Examples

person <- dplyr::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
)
cohort1 <- dplyr::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,
    "cohort1" = cohort1
  ),
  cdmName = "test"
)
cdm
cdm$cohort1 <- newCohortTable(table = cdm$cohort1)
cdm
settings(cdm$cohort1)
attrition(cdm$cohort1)
cohortCount(cdm$cohort1)

'conceptSetExpression' object constructor

Description

'conceptSetExpression' object constructor

Usage

newConceptSetExpression(x)

Arguments

Value

A conceptSetExpression

A new local source for the cdm

Description

A new local source for the cdm

Usage

newLocalSource()

Value

A list in the format of a cdm source

Examples

library(omopgenerics)
newLocalSource()

Create an omop table from a cdm table.

Description

Create an omop table from a cdm table.

Usage

newOmopTable(table, version = "5.3", cast = FALSE)

Arguments

Value

An omop_table object

'summarised_results' object constructor

Description

'summarised_results' object constructor

Usage

newSummarisedResult(x, settings = attr(x, "settings"))

Arguments

Value

A summarised_result object

Examples

library(dplyr)
library(omopgenerics)

x <- tibble(
  "result_id" = 1L,
  "cdm_name" = "cprd",
  "group_name" = "cohort_name",
  "group_level" = "acetaminophen",
  "strata_name" = "sex &&& age_group",
  "strata_level" = c("male &&& <40", "male &&& >=40"),
  "variable_name" = "number_subjects",
  "variable_level" = NA_character_,
  "estimate_name" = "count",
  "estimate_type" = "integer",
  "estimate_value" = c("5", "15"),
  "additional_name" = "overall",
  "additional_level" = "overall"
) |>
  newSummarisedResult()

x
settings(x)
summary(x)

x <- tibble(
  "result_id" = 1L,
  "cdm_name" = "cprd",
  "group_name" = "cohort_name",
  "group_level" = "acetaminophen",
  "strata_name" = "sex &&& age_group",
  "strata_level" = c("male &&& <40", "male &&& >=40"),
  "variable_name" = "number_subjects",
  "variable_level" = NA_character_,
  "estimate_name" = "count",
  "estimate_type" = "integer",
  "estimate_value" = c("5", "15"),
  "additional_name" = "overall",
  "additional_level" = "overall"
) |>
  newSummarisedResult(settings = tibble(
    result_id = 1L, result_type = "custom_summary", mock = TRUE, value = 5
  ))

x
settings(x)
summary(x)

Count the number of records that a cdm_table has.

Description

Count the number of records that a cdm_table has.

Usage

numberRecords(x)

Arguments

Value

An integer with the number of records in the table.

Examples

person <- dplyr::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
)
cdm <- cdmFromTables(
  tables = list("person" = person, "observation_period" = observation_period),
  cdmName = "test"
)

numberRecords(cdm$observation_period)

Count the number of subjects that a cdm_table has.

Description

Count the number of subjects that a cdm_table has.

Usage

numberSubjects(x)

Arguments

Value

An integer with the number of subjects in the table.

Examples

person <- dplyr::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
)
cdm <- cdmFromTables(
  tables = list("person" = person, "observation_period" = observation_period),
  cdmName = "test"
)

numberSubjects(cdm$observation_period)

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

Description

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

Usage

omopColumns(
  table,
  field = NULL,
  version = "5.3",
  onlyRequired = lifecycle::deprecated()
)

Arguments

Value

Character vector with the column names

Examples

library(omopgenerics)

omopColumns("person")

Return a table of omop cdm fields informations

Description

Return a table of omop cdm fields informations

Usage

omopTableFields(cdmVersion = "5.3")

Arguments

Value

a tibble contain informations on all the different fields in omop cdm.

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

Description

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

Usage

omopTables(version = "5.3")

Arguments

Value

Standard tables

Examples

library(omopgenerics)

omopTables()

Set estimates as columns

Description

Pivot the estimates as new columns in result table.

Usage

pivotEstimates(result, pivotEstimatesBy = "estimate_name", nameStyle = NULL)

Arguments

Value

A tibble.

Examples

{
  library(dplyr)
  library(omopgenerics)

  x <- tibble(
    "result_id" = 1L,
    "cdm_name" = c("cprd", "eunomia"),
    "group_name" = "cohort_name",
    "group_level" = "my_cohort",
    "strata_name" = "sex",
    "strata_level" = "male",
    "variable_name" = "Age group",
    "variable_level" = "10 to 50",
    "estimate_name" = "count",
    "estimate_type" = "numeric",
    "estimate_value" = "5",
    "additional_name" = "overall",
    "additional_level" = "overall"
  ) |>
    newSummarisedResult()

  x |>
    pivotEstimates()
}

Print a CDM reference object

Description

Print a CDM reference object

Usage

## S3 method for class 'cdm_reference'
print(x, ...)

Arguments

Value

Invisibly returns the input

Examples

library(omopgenerics)

cdm <- cdmFromTables(
  tables = list(
    "person" = dplyr::tibble(
      person_id = c(1, 2, 3), gender_concept_id = 0, year_of_birth = 1990,
      race_concept_id = 0, ethnicity_concept_id = 0
    ),
    "observation_period" = dplyr::tibble(
      observation_period_id = 1:3, person_id = 1:3,
      observation_period_start_date = as.Date("2000-01-01"),
      observation_period_end_date = as.Date("2023-12-31"),
      period_type_concept_id = 0
    )
  ),
  cdmName = "mock"
)

print(cdm)

Print a codelist

Description

Print a codelist

Usage

## S3 method for class 'codelist'
print(x, ...)

Arguments

Value

Invisibly returns the input

Examples

codes <- list("disease X" = c(1, 2, 3), "disease Y" = c(4, 5))
codes <- newCodelist(codes)
print(codes)

Print a codelist with details

Description

Print a codelist with details

Usage

## S3 method for class 'codelist_with_details'
print(x, ...)

Arguments

Value

Invisibly returns the input

Examples

codes <- list("disease X" = dplyr::tibble(
  concept_id = c(1, 2, 3),
  other = c("a", "b", "c")
))
codes <- newCodelistWithDetails(codes)
print(codes)

Print a concept set expression

Description

Print a concept set expression

Usage

## S3 method for class 'conceptSetExpression'
print(x, ...)

Arguments

Value

Invisibly returns the input

Examples

asthma_cs <- list(
  "asthma_narrow" = dplyr::tibble(
    "concept_id" = 1,
    "excluded" = FALSE,
    "descendants" = TRUE,
    "mapped" = FALSE
  ),
  "asthma_broad" = dplyr::tibble(
    "concept_id" = c(1, 2),
    "excluded" = FALSE,
    "descendants" = TRUE,
    "mapped" = FALSE
  )
)
asthma_cs <- newConceptSetExpression(asthma_cs)
print(asthma_cs)

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

Description

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

Usage

readSourceTable(cdm, name)

Arguments

Value

A cdm_reference with new table.

Update cohort attrition.

Description

Update cohort attrition.

Usage

recordCohortAttrition(cohort, reason, cohortId = NULL)

Arguments

Value

cohort_table with updated attrition.

Examples

library(omopgenerics)
library(dplyr, warn.conflicts = FALSE)

person <- tibble(
  person_id = 1, gender_concept_id = 0, year_of_birth = 1990,
  race_concept_id = 0, ethnicity_concept_id = 0
)
observation_period <- 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
)
cohort <- tibble(
  cohort_definition_id = c(1, 1, 1, 2),
  subject_id = 1,
  cohort_start_date = as.Date(c("2020-01-01", "2021-01-01", "2022-01-01", "2022-01-01")),
  cohort_end_date = as.Date(c("2020-01-01", "2021-01-01", "2022-01-01", "2022-01-01")),
)
cdm <- cdmFromTables(
  tables = list("person" = person, "observation_period" = observation_period),
  cdmName = "my_example_cdm",
  cohortTables = list("cohort1" = cohort)
)

cdm$cohort1
attrition(cdm$cohort1)

cdm$cohort1 <- cdm$cohort1 |>
  group_by(cohort_definition_id, subject_id) |>
  filter(cohort_start_date == min(cohort_start_date)) |>
  ungroup() |>
  compute(name = "cohort1", temporary = FALSE) |>
  recordCohortAttrition("Restrict to first observation")

cdm$cohort1
attrition(cdm$cohort1)

Objects exported from other packages

Description

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

dplyr

filter

generics

tidy

Required columns that the result tables must have.

Description

Required columns that the result tables must have.

Usage

resultColumns(table = "summarised_result")

Arguments

Value

Required columns

Examples

library(omopgenerics)

resultColumns()

Check if different packages version are used for summarise_results object

Description

Check if different packages version are used for summarise_results object

Usage

resultPackageVersion(result)

Arguments

Value

a summarised results object

Get settings from an object.

Description

Get settings from an object.

Usage

settings(x)

Arguments

Value

A table with the settings of the object.

Get cohort settings from a cohort_table object.

Description

Get cohort settings from a cohort_table object.

Usage

## S3 method for class 'cohort_table'
settings(x)

Arguments

Value

A table with the details of the cohort settings.

Examples

library(omopgenerics)
library(dplyr, warn.conflicts = FALSE)

person <- tibble(
  person_id = 1, gender_concept_id = 0, year_of_birth = 1990,
  race_concept_id = 0, ethnicity_concept_id = 0
)
observation_period <- 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
)
cohort <- tibble(
  cohort_definition_id = 1,
  subject_id = 1,
  cohort_start_date = as.Date("2010-01-01"),
  cohort_end_date = as.Date("2012-01-01")
)
cdm <- cdmFromTables(
  tables = list("person" = person, "observation_period" = observation_period),
  cdmName = "test",
  cohortTables = list("my_cohort" = cohort)
)

settings(cdm$my_cohort)

cdm$my_cohort <- cdm$my_cohort |>
  newCohortTable(cohortSetRef = tibble(
    cohort_definition_id = 1, cohort_name = "new_name"
  ))

settings(cdm$my_cohort)

Get settings from a summarised_result object.

Description

Get settings from a summarised_result object.

Usage

## S3 method for class 'summarised_result'
settings(x)

Arguments

Value

A table with the settings.

Examples

library(omopgenerics)
library(dplyr, warn.conflicts = FALSE)

person <- tibble(
  person_id = 1, gender_concept_id = 0, year_of_birth = 1990,
  race_concept_id = 0, ethnicity_concept_id = 0
)
observation_period <- 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
)
cohort <- tibble(
  cohort_definition_id = 1,
  subject_id = 1,
  cohort_start_date = as.Date("2010-01-01"),
  cohort_end_date = as.Date("2012-01-01")
)
cdm <- cdmFromTables(
  tables = list("person" = person, "observation_period" = observation_period),
  cdmName = "test",
  cohortTables = list("my_cohort" = cohort)
)

result <- summary(cdm$my_cohort)

settings(result)

Identify settings columns of a ⁠<summarised_result>⁠

Description

Identifies and returns the columns of the settings table obtained by using settings() in a ⁠<summarised_result>⁠ object.

Usage

settingsColumns(result, metadata = FALSE)

Arguments

Value

Vector with names of the settings columns

Examples

{
  library(dplyr)
  library(omopgenerics)

  x <- tibble(
    "result_id" = as.integer(c(1, 2)),
    "cdm_name" = c("cprd", "eunomia"),
    "group_name" = "cohort_name",
    "group_level" = "my_cohort",
    "strata_name" = "sex",
    "strata_level" = "male",
    "variable_name" = "Age group",
    "variable_level" = "10 to 50",
    "estimate_name" = "count",
    "estimate_type" = "numeric",
    "estimate_value" = "5",
    "additional_name" = "overall",
    "additional_level" = "overall"
  ) |>
    newSummarisedResult(settings = tibble(
      "result_id" = c(1, 2), "custom" = c("A", "B")
    ))

  x

  x |> settingsColumns()
}

Get the source type of an object.

Description

Get the source type of an object.

Usage

sourceType(x)

Arguments

Value

A character vector that defines the type of cdm_source.

Split additional_name and additional_level columns

Description

Pivots the input dataframe so the values of the column additional_name are transformed into columns that contain values from the additional_level column.

Usage

splitAdditional(result, keep = FALSE, fill = "overall")

Arguments

Value

A dataframe.

Examples

{
  library(dplyr)
  library(omopgenerics)

  x <- tibble(
    "result_id" = as.integer(c(1, 2)),
    "cdm_name" = c("cprd", "eunomia"),
    "group_name" = "cohort_name",
    "group_level" = "my_cohort",
    "strata_name" = "sex",
    "strata_level" = "male",
    "variable_name" = "Age group",
    "variable_level" = "10 to 50",
    "estimate_name" = "count",
    "estimate_type" = "numeric",
    "estimate_value" = "5",
    "additional_name" = "overall",
    "additional_level" = "overall"
  ) |>
    newSummarisedResult(settings = tibble(
      "result_id" = c(1, 2), "custom" = c("A", "B")
    ))

  x

  x |> splitAdditional()
}

Split all pairs name-level into columns.

Description

Pivots the input dataframe so any pair name-level columns are transformed into columns (name) that contain values from the corresponding level.

Usage

splitAll(result, keep = FALSE, fill = "overall", exclude = "variable")

Arguments

Value

A dataframe with group, strata and additional as columns.

Examples

{
  library(dplyr)
  library(omopgenerics)

  x <- tibble(
    "result_id" = as.integer(c(1, 2)),
    "cdm_name" = c("cprd", "eunomia"),
    "group_name" = "cohort_name",
    "group_level" = "my_cohort",
    "strata_name" = "sex",
    "strata_level" = "male",
    "variable_name" = "Age group",
    "variable_level" = "10 to 50",
    "estimate_name" = "count",
    "estimate_type" = "numeric",
    "estimate_value" = "5",
    "additional_name" = "overall",
    "additional_level" = "overall"
  ) |>
    newSummarisedResult(settings = tibble(
      "result_id" = c(1, 2), "custom" = c("A", "B")
    ))

  x

  x |> splitAll()
}

Split group_name and group_level columns

Description

Pivots the input dataframe so the values of the column group_name are transformed into columns that contain values from the group_level column.

Usage

splitGroup(result, keep = FALSE, fill = "overall")

Arguments

Value

A dataframe.

Examples

{
  library(dplyr)
  library(omopgenerics)

  x <- tibble(
    "result_id" = as.integer(c(1, 2)),
    "cdm_name" = c("cprd", "eunomia"),
    "group_name" = "cohort_name",
    "group_level" = "my_cohort",
    "strata_name" = "sex",
    "strata_level" = "male",
    "variable_name" = "Age group",
    "variable_level" = "10 to 50",
    "estimate_name" = "count",
    "estimate_type" = "numeric",
    "estimate_value" = "5",
    "additional_name" = "overall",
    "additional_level" = "overall"
  ) |>
    newSummarisedResult(settings = tibble(
      "result_id" = c(1, 2), "custom" = c("A", "B")
    ))

  x

  x |> splitGroup()
}

Split strata_name and strata_level columns

Description

Pivots the input dataframe so the values of the column strata_name are transformed into columns that contain values from the strata_level column.

Usage

splitStrata(result, keep = FALSE, fill = "overall")

Arguments

Value

A dataframe.

Examples

{
  library(dplyr)
  library(omopgenerics)

  x <- tibble(
    "result_id" = as.integer(c(1, 2)),
    "cdm_name" = c("cprd", "eunomia"),
    "group_name" = "cohort_name",
    "group_level" = "my_cohort",
    "strata_name" = "sex",
    "strata_level" = "male",
    "variable_name" = "Age group",
    "variable_level" = "10 to 50",
    "estimate_name" = "count",
    "estimate_type" = "numeric",
    "estimate_value" = "5",
    "additional_name" = "overall",
    "additional_level" = "overall"
  ) |>
    newSummarisedResult(settings = tibble(
      "result_id" = c(1, 2), "custom" = c("A", "B")
    ))

  x

  x |> splitStrata()
}

Status of the indexes

Description

Usage

statusIndexes(cdm, name = NULL)

Arguments

Value

A tibble with 3 columns: table_class class of the table, table_name name of the table, index index definition, and index_status status of the index, either: 'missing', 'extra', 'present'.

Identify variables in strata_name column

Description

Identifies and returns the unique values in strata_name column.

Usage

strataColumns(result)

Arguments

Value

Unique values of the strata name column.

Examples

{
  library(dplyr)
  library(omopgenerics)

  x <- tibble(
    "result_id" = as.integer(c(1, 2)),
    "cdm_name" = c("cprd", "eunomia"),
    "group_name" = "cohort_name",
    "group_level" = "my_cohort",
    "strata_name" = "sex",
    "strata_level" = "male",
    "variable_name" = "Age group",
    "variable_level" = "10 to 50",
    "estimate_name" = "count",
    "estimate_type" = "numeric",
    "estimate_value" = "5",
    "additional_name" = "overall",
    "additional_level" = "overall"
  ) |>
    newSummarisedResult(settings = tibble(
      "result_id" = c(1, 2), "custom" = c("A", "B")
    ))

  x

  x |> strataColumns()
}

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

Description

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

Usage

summariseLogFile(
  logFile = getOption("omopgenerics.logFile"),
  cdmName = "unknown"
)

Arguments

Value

A summarise_result with the information of the log file.

Examples

library(dplyr)

logFile <- tempfile(pattern = "log_{date}_{time}", fileext = ".txt")
createLogFile(logFile = logFile)

logMessage("Starting analysis")
1 + 1
logMessage("Analysis finished")

res <- summariseLogFile()

glimpse(res)

tidy(res)

Summary a cdm reference

Description

Summary a cdm reference

Usage

## S3 method for class 'cdm_reference'
summary(object, ...)

Arguments

Value

A summarised_result object with a summary of the data contained in the cdm.

Examples

library(dplyr, warn.conflicts = FALSE)

person <- tibble(
  person_id = 1, gender_concept_id = 0, year_of_birth = 1990,
  race_concept_id = 0, ethnicity_concept_id = 0
)
observation_period <- 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
)
cdm <- cdmFromTables(
  tables = list("person" = person, "observation_period" = observation_period),
  cdmName = "test"
)

summary(cdm)

Summarise a cdm_source object

Description

Summarise a cdm_source object

Usage

## S3 method for class 'cdm_source'
summary(object, ...)

Arguments

Value

A list of properties of the cdm_source object.

Examples

summary(newLocalSource())

Summary a generated cohort set

Description

Summary a generated cohort set

Usage

## S3 method for class 'cohort_table'
summary(object, ...)

Arguments

Value

A summarised_result object with a summary of a cohort_table.

Examples

library(dplyr, warn.conflicts = FALSE)

person <- tibble(
  person_id = 1, gender_concept_id = 0, year_of_birth = 1990,
  race_concept_id = 0, ethnicity_concept_id = 0
)
observation_period <- 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
)
cdm <- cdmFromTables(
  tables = list("person" = person, "observation_period" = observation_period),
  cdmName = "test",
  cohortTables = list("cohort1" = tibble(
    cohort_definition_id = 1,
    subject_id = 1,
    cohort_start_date = as.Date("2010-01-01"),
    cohort_end_date = as.Date("2010-01-05")
  ))
)

summary(cdm$cohort1)

Summary a summarised_result

Description

Summary a summarised_result

Usage

## S3 method for class 'summarised_result'
summary(object, ...)

Arguments

Value

A summary of the result_types contained in a summarised_result object.

Examples

library(dplyr, warn.conflicts = FALSE)

person <- tibble(
  person_id = 1, gender_concept_id = 0, year_of_birth = 1990,
  race_concept_id = 0, ethnicity_concept_id = 0
)
observation_period <- 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
)
cdm <- cdmFromTables(
  tables = list("person" = person, "observation_period" = observation_period),
  cdmName = "test"
)

result <- summary(cdm)

summary(result)

Function to suppress counts in result objects

Description

Function to suppress counts in result objects

Usage

suppress(result, minCellCount = 5)

Arguments

Value

Table with suppressed counts

Function to suppress counts in result objects

Description

Function to suppress counts in result objects

Usage

## S3 method for class 'summarised_result'
suppress(result, minCellCount = 5)

Arguments

Value

summarised_result with suppressed counts.

Examples

library(dplyr, warn.conflicts = FALSE)
library(omopgenerics)

my_result <- tibble(
  "result_id" = "1",
  "cdm_name" = "mock",
  "result_type" = "summarised_characteristics",
  "package_name" = "omopgenerics",
  "package_version" = as.character(utils::packageVersion("omopgenerics")),
  "group_name" = "overall",
  "group_level" = "overall",
  "strata_name" = c(rep("overall", 6), rep("sex", 3)),
  "strata_level" = c(rep("overall", 6), "male", "female", "female"),
  "variable_name" = c(
    "number records", "age_group", "age_group",
    "age_group", "age_group", "my_variable", "number records", "age_group",
    "age_group"
  ),
  "variable_level" = c(
    NA, "<50", "<50", ">=50", ">=50", NA, NA,
    "<50", "<50"
  ),
  "estimate_name" = c(
    "count", "count", "percentage", "count", "percentage",
    "random", "count", "count", "percentage"
  ),
  "estimate_type" = c(
    "integer", "integer", "percentage", "integer",
    "percentage", "numeric", "integer", "integer", "percentage"
  ),
  "estimate_value" = c("10", "5", "50", "3", "30", "1", "3", "12", "6"),
  "additional_name" = "overall",
  "additional_level" = "overall"
)
my_result <- newSummarisedResult(my_result)
my_result |> glimpse()
my_result <- suppress(my_result, minCellCount = 5)
my_result |> glimpse()

Get the table name of a cdm_table.

Description

Get the table name of a cdm_table.

Usage

tableName(table)

Arguments

Value

A character with the name.

Examples

library(omopgenerics)
library(dplyr, warn.conflicts = FALSE)

cdm <- cdmFromTables(
  tables = list(
    "person" = tibble(
      person_id = c(1, 2, 3), gender_concept_id = 0, year_of_birth = 1990,
      race_concept_id = 0, ethnicity_concept_id = 0
    ),
    "observation_period" = tibble(
      observation_period_id = 1:3, person_id = 1:3,
      observation_period_start_date = as.Date("2000-01-01"),
      observation_period_end_date = as.Date("2023-12-31"),
      period_type_concept_id = 0
    )
  ),
  cdmName = "mock"
)

tableName(cdm$person)

Get the table source of a cdm_table.

Description

Get the table source of a cdm_table.

Usage

tableSource(table)

Arguments

Value

A cdm_source object.

Examples

library(omopgenerics)
library(dplyr, warn.conflicts = FALSE)

cdm <- cdmFromTables(
  tables = list(
    "person" = tibble(
      person_id = c(1, 2, 3), gender_concept_id = 0, year_of_birth = 1990,
      race_concept_id = 0, ethnicity_concept_id = 0
    ),
    "observation_period" = tibble(
      observation_period_id = 1:3, person_id = 1:3,
      observation_period_start_date = as.Date("2000-01-01"),
      observation_period_end_date = as.Date("2023-12-31"),
      period_type_concept_id = 0
    )
  ),
  cdmName = "mock"
)

tableSource(cdm$person)

Turn a ⁠<summarised_result>⁠ object into a tidy tibble

Description

Provides tools for obtaining a tidy version of a ⁠<summarised_result>⁠ object. This tidy version will include the settings as columns, estimate_value will be pivotted into columns using estimate_name as names, and group, strata, and additional will be splitted.

Usage

## S3 method for class 'summarised_result'
tidy(x, ...)

Arguments

Value

A tibble.

Examples

{
  library(dplyr)
  library(omopgenerics)

  x <- tibble(
    "result_id" = as.integer(c(1, 2)),
    "cdm_name" = c("cprd", "eunomia"),
    "group_name" = "cohort_name",
    "group_level" = "my_cohort",
    "strata_name" = "sex",
    "strata_level" = "male",
    "variable_name" = "Age group",
    "variable_level" = "10 to 50",
    "estimate_name" = "count",
    "estimate_type" = "numeric",
    "estimate_value" = "5",
    "additional_name" = "overall",
    "additional_level" = "overall"
  ) |>
    newSummarisedResult(settings = tibble(
      "result_id" = c(1, 2), "custom" = c("A", "B")
    ))

  x

  x |> tidy()
}

Identify tidy columns of a ⁠<summarised_result>⁠

Description

Identifies and returns the columns that the tidy version of the ⁠<summarised_result>⁠ will have.

Usage

tidyColumns(result)

Arguments

Value

Table columns after applying tidy() function to a ⁠<summarised_result>⁠.

Examples

{
  library(dplyr)
  library(omopgenerics)

  x <- tibble(
    "result_id" = as.integer(c(1, 2)),
    "cdm_name" = c("cprd", "eunomia"),
    "group_name" = "cohort_name",
    "group_level" = "my_cohort",
    "strata_name" = "sex",
    "strata_level" = "male",
    "variable_name" = "Age group",
    "variable_level" = "10 to 50",
    "estimate_name" = "count",
    "estimate_type" = "numeric",
    "estimate_value" = "5",
    "additional_name" = "overall",
    "additional_level" = "overall"
  ) |>
    newSummarisedResult(settings = tibble(
      "result_id" = c(1, 2), "custom" = c("A", "B")
    ))

  x

  x |> tidyColumns()
}

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

Description

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

Usage

tmpPrefix()

Value

A temporary prefix.

Examples

library(omopgenerics)
tmpPrefix()

Convert a character vector to snake case

Description

Convert a character vector to snake case

Usage

toSnakeCase(x)

Arguments

Value

A snake_case vector

Examples

toSnakeCase("myVariable")

toSnakeCase(c("cohort1", "Cohort22b"))

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

Description

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

Usage

transformToSummarisedResult(
  x,
  group = character(),
  strata = character(),
  additional = character(),
  estimates = character(),
  settings = character()
)

Arguments

Value

A <summarised_result> object.

Examples

x <- dplyr::tibble(
  cohort_name = c("cohort1", "cohort2"),
  variable_name = "age",
  mean = c(50, 45.3),
  median = c(55L, 44L)
)

transformToSummarisedResult(
  x = x,
  group = c("cohort_name"),
  estimates = c("mean", "median")
)

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

Description

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

Usage

uniqueId(n = 1, exclude = character(), nChar = 3, prefix = "id_")

Arguments

Value

A character vector with n unique identifiers.

Create a unique table name

Description

Create a unique table name

Usage

uniqueTableName(prefix = "")

Arguments

Value

A string that can be used as a dbplyr temp table name

Examples

library(omopgenerics)
uniqueTableName()

Unite one or more columns in additional_name-additional_level format

Description

Unites targeted table columns into additional_name-additional_level columns.

Usage

uniteAdditional(
  x,
  cols = character(0),
  keep = FALSE,
  ignore = c(NA, "overall")
)

Arguments

Value

A tibble with the new columns.

Examples

x <- dplyr::tibble(
  variable = "number subjects",
  value = c(10, 15, 40, 78),
  sex = c("Male", "Female", "Male", "Female"),
  age_group = c("<40", ">40", ">40", "<40")
)

x |>
  uniteAdditional(c("sex", "age_group"))

Unite one or more columns in group_name-group_level format

Description

Unites targeted table columns into group_name-group_level columns.

Usage

uniteGroup(x, cols = character(0), keep = FALSE, ignore = c(NA, "overall"))

Arguments

Value

A tibble with the new columns.

Examples

x <- dplyr::tibble(
  variable = "number subjects",
  value = c(10, 15, 40, 78),
  sex = c("Male", "Female", "Male", "Female"),
  age_group = c("<40", ">40", ">40", "<40")
)

x |>
  uniteGroup(c("sex", "age_group"))

Unite one or more columns in strata_name-strata_level format

Description

Unites targeted table columns into strata_name-strata_level columns.

Usage

uniteStrata(x, cols = character(0), keep = FALSE, ignore = c(NA, "overall"))

Arguments

Value

A tibble with the new columns.

Examples

x <- dplyr::tibble(
  variable = "number subjects",
  value = c(10, 15, 40, 78),
  sex = c("Male", "Female", "Male", "Female"),
  age_group = c("<40", ">40", ">40", "<40")
)

x |>
  uniteStrata(c("sex", "age_group"))

Validate if a cdm_table is a valid achilles table.

Description

Validate if a cdm_table is a valid achilles table.

Usage

validateAchillesTable(
  table,
  version = NULL,
  cast = FALSE,
  call = parent.frame()
)

Arguments

Value

invisible achilles table

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.

Description

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.

Usage

validateAgeGroupArgument(
  ageGroup,
  multipleAgeGroup = TRUE,
  overlap = FALSE,
  null = TRUE,
  ageGroupName = "age_group",
  call = parent.frame()
)

Arguments

Value

validate ageGroup

Examples

validateAgeGroupArgument(list(c(0, 39), c(40, Inf)))

Validate if an object in a valid cdm_reference.

Description

Validate if an object in a valid cdm_reference.

Usage

validateCdmArgument(
  cdm,
  checkOverlapObservation = FALSE,
  checkStartBeforeEndObservation = FALSE,
  checkPlausibleObservationDates = FALSE,
  checkPerson = FALSE,
  requiredTables = character(),
  validation = "error",
  call = parent.frame()
)

Arguments

Value

A cdm_reference object

Examples

cdm <- cdmFromTables(
  tables = list(
    "person" = dplyr::tibble(
      person_id = c(1, 2, 3), gender_concept_id = 0, year_of_birth = 1990,
      race_concept_id = 0, ethnicity_concept_id = 0
    ),
    "observation_period" = dplyr::tibble(
      observation_period_id = 1:3, person_id = 1:3,
      observation_period_start_date = as.Date("2000-01-01"),
      observation_period_end_date = as.Date("2023-12-31"),
      period_type_concept_id = 0
    )
  ),
  cdmName = "mock"
)

validateCdmArgument(cdm)

Validate if a table is a valid cdm_table object.

Description

Validate if a table is a valid cdm_table object.

Usage

validateCdmTable(table, name = NULL, call = parent.frame())

Arguments

Value

The table or an error message.

Validate a cohort table input.

Description

Validate a cohort table input.

Usage

validateCohortArgument(
  cohort,
  checkEndAfterStart = FALSE,
  checkOverlappingEntries = FALSE,
  checkMissingValues = FALSE,
  checkInObservation = FALSE,
  checkAttributes = FALSE,
  checkPermanentTable = FALSE,
  dropExtraColumns = FALSE,
  validation = "error",
  call = parent.frame()
)

Arguments

Examples

cdm <- cdmFromTables(
  tables = list(
    "person" = dplyr::tibble(
      person_id = c(1, 2, 3), gender_concept_id = 0, year_of_birth = 1990,
      race_concept_id = 0, ethnicity_concept_id = 0
    ),
    "observation_period" = dplyr::tibble(
      observation_period_id = 1:3, person_id = 1:3,
      observation_period_start_date = as.Date("2000-01-01"),
      observation_period_end_date = as.Date("2023-12-31"),
      period_type_concept_id = 0
    )
   ),
   cohortTables = list(
    cohort = dplyr::tibble(
      cohort_definition_id = 1L,
      subject_id = 1L,
      cohort_start_date = as.Date("2020-01-01"),
      cohort_end_date = as.Date("2021-02-10")
    )
  ),
  cdmName = "mock"
)

validateCohortArgument(cdm$cohort)

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).

Description

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).

Usage

validateCohortIdArgument(
  cohortId,
  cohort,
  null = TRUE,
  validation = "error",
  call = parent.frame()
)

Arguments

Examples

cdm <- cdmFromTables(
  tables = list(
    "person" = dplyr::tibble(
      person_id = c(1, 2, 3), gender_concept_id = 0, year_of_birth = 1990,
      race_concept_id = 0, ethnicity_concept_id = 0
    ),
    "observation_period" = dplyr::tibble(
      observation_period_id = 1:3, person_id = 1:3,
      observation_period_start_date = as.Date("2000-01-01"),
      observation_period_end_date = as.Date("2023-12-31"),
      period_type_concept_id = 0
    )
   ),
   cohortTables = list(
    cohort = dplyr::tibble(
      cohort_definition_id = 1L,
      subject_id = 1L,
      cohort_start_date = as.Date("2020-01-01"),
      cohort_end_date = as.Date("2021-02-10")
    )
  ),
  cdmName = "mock"
)

validateCohortIdArgument(NULL, cdm$cohort)
validateCohortIdArgument(1L, cdm$cohort)
validateCohortIdArgument(2L, cdm$cohort, validation = "warning")

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

Description

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

Usage

validateColumn(
  column,
  x,
  type = c("character", "date", "logical", "numeric", "integer"),
  validation = "error",
  call = parent.frame()
)

Arguments

Value

the validated name

Examples

x <- dplyr::tibble(a = 1, b = "xxx")

validateColumn("a", x, validation = "warning")
validateColumn("a", x, type = "character", validation = "warning")
validateColumn("a", x, type = "numeric", validation = "warning")
validateColumn("not_existing", x, type = "numeric", validation = "warning")

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.

Description

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.

Usage

validateConceptSetArgument(
  conceptSet,
  cdm = NULL,
  validation = "error",
  call = parent.frame()
)

Arguments

Value

A codelist object.

Examples

conceptSet <- list(disease_x = c(1L, 2L))
validateConceptSetArgument(conceptSet)

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.

Description

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.

Usage

validateNameArgument(
  name,
  cdm = NULL,
  validation = "error",
  null = FALSE,
  call = parent.frame()
)

Arguments

Examples

# this is a validate name
name <- "my_new_table"
validateNameArgument(name)

# this is not
name <- "myTableNAME"
validateNameArgument(name, validation = "warning")

Validate if two columns are valid Name-Level pair.

Description

Validate if two columns are valid Name-Level pair.

Usage

validateNameLevel(
  x,
  prefix,
  sep = " &&& ",
  validation = "error",
  call = parent.frame()
)

Arguments

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.

Description

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.

Usage

validateNameStyle(nameStyle, ..., call = parent.frame())

Arguments

Value

invisible nameStyle.

Examples

validateNameStyle(
  nameStyle = "hi_{cohort_name}",
  cohortName = c("cohort1", "cohort2"),
  otherVariable = c("only 1 value")
)

## Not run: 
validateNameStyle(
  nameStyle = "hi_{cohort_name}",
  cohortName = c("cohort1", "cohort2"),
  otherVariable = c("value1", "value2")
)

## End(Not run)
validateNameStyle(
  nameStyle = "{other_variable}_hi_{cohort_name}",
  cohortName = c("cohort1", "cohort2"),
  otherVariable = c("value1", "value2")
)

Validate a new column of a table

Description

Validate a new column of a table

Usage

validateNewColumn(table, column, validation = "warning", call = parent.frame())

Arguments

Value

table without conflicting columns.

Examples

x <- dplyr::tibble(
  column1 = c(1L, 2L),
  column2 = c("a", "b")
)
validateNewColumn(x, "not_exiting_column")
validateNewColumn(x, "column1")

Validate an omop_table

Description

Validate an omop_table

Usage

validateOmopTable(
  omopTable,
  version = NULL,
  cast = FALSE,
  call = parent.frame()
)

Arguments

Value

An omop_table object.

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

Description

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

Usage

validateResultArgument(
  result,
  checkNoDuplicates = FALSE,
  checkNameLevel = FALSE,
  checkSuppression = FALSE,
  validation = "error",
  call = parent.frame()
)

Arguments

Value

summarise result object

Examples

x <- dplyr::tibble(
  "result_id" = 1L,
  "cdm_name" = "eunomia",
  "group_name" = "cohort_name",
  "group_level" = "my_cohort",
  "strata_name" = c("sex", "sex &&& age_group", "sex &&& year"),
  "strata_level" = c("Female", "Male &&& <40", "Female &&& 2010"),
  "variable_name" = "number subjects",
  "variable_level" = NA_character_,
  "estimate_name" = "count",
  "estimate_type" = "integer",
  "estimate_value" = c("100", "44", "14"),
  "additional_name" = "overall",
  "additional_level" = "overall"
) |>
  newSummarisedResult()

validateResultArgument(x)

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

Description

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

Usage

validateStrataArgument(strata, table, call = parent.frame())

Arguments

Value

The same strata input or an error if the input is incorrect.

Examples

strata <- list("age", "sex", c("age", "sex"))
x <- dplyr::tibble(age = 30L, sex = "Female")

validateStrataArgument(strata, x)

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.

Description

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.

Usage

validateWindowArgument(window, snakeCase = TRUE, call = parent.frame())

Arguments

Value

time window

Examples

validateWindowArgument(list(c(0, 15), c(-Inf, Inf)))
validateWindowArgument(list(c(0, 15), c(-Inf, Inf)), snakeCase = FALSE)