Package 'omopgenerics'

Title: 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.
Authors: Martí Català [aut, cre] , Edward Burn [aut] , Mike Du [ctb] , Yuchen Guo [ctb] , Adam Black [ctb] , Marta Alcalde-Herraiz [ctb]
Maintainer: Martí Català <[email protected]>
License: Apache License (>= 2)
Version: 0.4.1
Built: 2024-12-05 19:41:52 UTC
Source: CRAN

Help Index

Subset a cdm reference object.

Description

Subset a cdm reference object.

Usage

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

Arguments

x

A cdm reference
name

The name or index of the table to extract from the cdm object.

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

cdm

A cdm reference.
name

Name where to assign the new table.
value

Table with the same source than the cdm object.

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

x

A cdm reference.
name

The name of the table to extract from the cdm object.

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 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

cdm

A cdm reference.
name

Name where to assign the new table.
value

Table with the same source than the cdm object.

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

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

table

Table for which to see the required columns. One of "achilles_analysis", "achilles_results", or "achilles_results_dist".
version

Version of the OMOP Common Data Model.
onlyRequired

deprecated.

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

version

Version of the OMOP Common Data Model.

Value

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

Examples

library(omopgenerics)
achillesTables()

Identify variables in additional_name column

Description

Identifies and returns the unique values in additional_name column.

Usage

additionalColumns(result)

Arguments

result

A tibble.

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()
}

Add settings columns to a ⁠<summarised_result>⁠ object

Description

Add settings columns to a ⁠<summarised_result>⁠ object

Usage

addSettings(result, settingsColumn = settingsColumns(result))

Arguments

result

A ⁠<summarised_result>⁠ object.
settingsColumn

Settings to be added as columns, by default settingsColumns(result) will be added. If NULL or empty character vector, no settings will be added.

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()
}

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

x

Variable to check.
length

Required length. If NULL length is not checked.
na

Whether it can contain NA values.
null

Whether it can be NULL.
unique

Whether it has to contain unique elements.
named

Whether it has to be named.
minNumCharacter

Minimum number of characters that all elements must have.
call

Call argument that will be passed to cli error message.
msg

Custom error message.

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

x

Variable to check.
choices

Options that x is allowed to be.
length

Required length. If NULL length is not checked.
na

Whether it can contain NA values.
null

Whether it can be NULL.
unique

Whether it has to contain unique elements.
named

Whether it has to be named.
call

Call argument that will be passed to cli error message.
msg

Custom error message.

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

x

To check.
class

Expected class or classes.
length

Required length. If NULL length is not checked.
null

Whether it can be NULL.
all

Whether it should have all the classes or only at least one of them.
extra

Whether the object can have extra classes.
call

Call argument that will be passed to cli.
msg

Custom error message.

Assert Date

Description

Assert Date

Usage

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

Arguments

x

Expression to check.
length

Required length.
na

Whether it can contain NA values.
null

Whether it can be NULL.
unique

Whether it has to contain unique elements.
named

Whether it has to be named.
call

Call argument that will be passed to cli error message.
msg

Custom error message.

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

x

Variable to check.
length

Required length. If NULL length is not checked.
na

Whether it can contain NA values.
null

Whether it can be NULL.
unique

Whether it has to contain unique elements.
named

Whether it has to be named.
class

Class that the elements must have.
call

Call argument that will be passed to cli error message.
msg

Custom error message.

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

x

Variable to check.
length

Required length. If NULL length is not checked.
na

Whether it can contain NA values.
null

Whether it can be NULL.
named

Whether it has to be named.
call

Call argument that will be passed to cli error message.
msg

Custom error message.

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

x

Variable to check.
integerish

Whether it has to be an integer
min

Minimum value that the object can be.
max

Maximum value that the object can be.
length

Required length. If NULL length is not checked.
na

Whether it can contain NA values.
null

Whether it can be NULL.
unique

Whether it has to contain unique elements.
named

Whether it has to be named.
call

Call argument that will be passed to cli error message.
msg

Custom error message.

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

x

Variable to check.
class

A class that the table must have: "tbl", "data.fram", "tbl_sql", ...
numberColumns

Number of columns that it has to contain.
numberRows

Number of rows that it has to contain.
columns

Name of the columns required.
allowExtraColumns

Whether extra columns are allowed.
null

Whether it can be NULL.
unique

Whether it has to contain unique rows.
call

Call argument that will be passed to cli error message.
msg

Custom error message.

Assert that an expression is TRUE.

Description

Assert that an expression is TRUE.

Usage

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

Arguments

x

Expression to check.
null

Whether it can be NULL.
call

Call argument that will be passed to cli error message.
msg

Custom error message.

Get attrition from an object.

Description

Get attrition from an object.

Usage

attrition(x)

Arguments

x

An object for which to get an attrition summary.

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

x

A cohort_table

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

...

Objects to bind.

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

...

Generated cohort set objects to bind. At least two must be provided.
name

Name of the new generated cohort set.

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

...

summarised_result objects

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

Create a cdm object from local tables

Description

Create a cdm object from local tables

Usage

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

Arguments

tables

List of tables to be part of the cdm object.
cdmName

Name of the cdm object.
cohortTables

List of tables that contains cohort, cohort_set and cohort_attrition can be provided as attributes.
cdmVersion

Version of the cdm_reference

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

x

A cdm_reference or cdm_table object.

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

table

A cdm_table.

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

cdm

A cdm_reference object.
...

Selection of tables to use, it supports tidyselect expressions.

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

x

Object to obtain the cdmSource.
cdm

Deprecated, use x please.

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

[Deprecated]

Usage

cdmSourceType(cdm)

Arguments

cdm

A cdm_reference object.

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

src

A cdm_source object.
value

A table that shares source with the cdm_reference object.

Value

A cdm_table.

Get the version of an object.

Description

Get the version of an object.

Usage

cdmVersion(x)

Arguments

x

Object to know the cdm version of an object.

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

[Deprecated]

Usage

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

Arguments

cohort

cohort_table object.
checkEndAfterStart

If TRUE a check that all cohort end dates come on or after cohort start date will be performed.
checkOverlappingEntries

If TRUE a check that no individuals have overlapping cohort entries will be performed.
checkMissingValues

If TRUE a check that there are no missing values in required fields will be performed.
checkInObservation

If TRUE a check that cohort entries are within the individuals observation periods will be performed.
type

Can be either "error" or "warning". If "error" any check failure will result in an error, whereas if "warning" any check failure will result in a warning.
call

The call for which to return the error message.

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,
  type = c("index event", "inclusion criteria", "exclusion criteria", "exit criteria")
)

Arguments

cohortTable

A cohort_table object.
cohortId

A particular cohort definition id that is present in the cohort table.
type

The reason for the codelist. Can be "index event", "inclusion criteria", or "exit criteria".

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),
                                type = "index event"
                              ))
cohortCodelist(cdm$cohort1, cohortId = 1, type = "index event")

Required columns for a generated cohort set.

Description

Required columns for a generated cohort set.

Usage

cohortColumns(table, version = "5.3")

Arguments

table

Either cohort, cohort_set or cohort_attrition
version

Version of the OMOP Common Data Model.

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

cohort

A cohort_table object.

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

version

Version of the OMOP Common Data Model.

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

x

A cdm_reference object.
...

For compatibility only, not used.

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

x

cohort_table object.
...

Not used (for compatibility).

Value

A data frame with the cohort_table

Provide all combinations of strata levels.

Description

Provide all combinations of strata levels.

Usage

combineStrata(levels)

Arguments

levels

Vector of all strata levels to combine.

Value

A vector of all combinations of strata.

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

x

Table in the cdm.
name

Name to store the table with.
temporary

Whether to store table temporarily (TRUE) or permanently (FALSE).
overwrite

Whether to overwrite previously existing table with name same.
logPrefix

Prefix to use when saving a log file.
...

For compatibility (not used).

Value

Reference to a table in the cdm

Drop a table from a cdm object.

Description

Drop a table from a cdm object.

Usage

dropSourceTable(cdm, name)

Arguments

cdm

A cdm reference.
name

Name(s) of the table(s) to insert. Tidyselect statements are supported.

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

cdm

A cdm reference.
name

Name(s) of the table(s) to drop Tidyselect statements are supported.

Value

The cdm reference.

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

cdm <- dropTable(cdm = cdm, name = "cohort1")

cdm

Create an empty achilles table

Description

Create an empty achilles table

Usage

emptyAchillesTable(cdm, name)

Arguments

cdm

A cdm_reference to create the table.
name

Name of the table to create.

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

cdmName

Name of the cdm_reference
cdmVersion

Version of the cdm_reference

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

cdm

A cdm_reference to create the table.
name

Name of the table to create.
overwrite

Whether to overwrite an existent table.

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

cdm

A cdm_reference to create the table.
name

Name of the table to create.

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

settings

Tibble/data.frame with the settings of the empty summarised_result. It has to contain at least result_id column.

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()

Export a codelist object.

Description

Export a codelist object.

Usage

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

Arguments

x

A codelist
path

Path to where files will be created.
type

Type of files to export. Currently 'json' and 'csv' are supported.

Value

Files with codelists

Export a concept set expression.

Description

Export a concept set expression.

Usage

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

Arguments

x

A concept set expression
path

Path to where files will be created.
type

Type of files to export. Currently 'json' and 'csv' are supported.

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()
)

Arguments

...

A set of summarised_result objects.
minCellCount

Minimum count for suppression purposes.
fileName

Name of the file that will be created. Use {cdm_name} to refer to the cdmName of the objects and {date} to add the export date.
path

Path where to create the csv file. It is ignored if fileName it is a full name with path included.

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

result

A ⁠<summarised_result>⁠ object.
...

Expressions that return a logical value (additionalColumns() are used to evaluate the expression), and are defined in terms of the variables in .data. If multiple expressions are included, they are combined with the & operator. Only rows for which all conditions evaluate to TRUE are kept.

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

result

A ⁠<summarised_result>⁠ object.
...

Expressions that return a logical value (groupColumns() are used to evaluate the expression), and are defined in terms of the variables in .data. If multiple expressions are included, they are combined with the & operator. Only rows for which all conditions evaluate to TRUE are kept.

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

result

A ⁠<summarised_result>⁠ object.
...

Expressions that return a logical value (columns in settings are used to evaluate the expression), and are defined in terms of the variables in .data. If multiple expressions are included, they are combined with the & operator. Only rows for which all conditions evaluate to TRUE are kept.

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

result

A ⁠<summarised_result>⁠ object.
...

Expressions that return a logical value (strataColumns() are used to evaluate the expression), and are defined in terms of the variables in .data. If multiple expressions are included, they are combined with the & operator. Only rows for which all conditions evaluate to TRUE are kept.

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

cohort

A cohort_table object.
cohortName

Names of the cohort of interest. If NULL all cohort names are shown.

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

cohort

A cohort_table object.
cohortId

Cohort definition id of interest. If NULL all cohort ids are shown.

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

x

A table.
call

A call argument passed to cli functions.

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

result

A tibble.

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

path

Path to where files will be created.
type

Type of files to export. Currently 'json' and 'csv' are supported.

Value

A codelist

Import a concept set expression.

Description

Import a concept set expression.

Usage

importConceptSetExpression(path, type = "json")

Arguments

path

Path to where files will be created.
type

Type of files to export. Currently 'json' and 'csv' are supported.

Value

A concept set expression

Import a set of summarised results.

Description

Import a set of summarised results.

Usage

importSummarisedResult(path, recursive = FALSE)

Arguments

path

Path to directory with CSV files containing summarised results or to a specific CSV file with a summarised result.
recursive

If TRUE and path is a directory, search for files will recurse into directories

Value

A summarised result

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

[Deprecated]

Usage

insertFromSource(cdm, value)

Arguments

cdm

A cdm_reference object.
value

A table that shares source with the cdm_reference object.

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

cdm

A cdm reference or the source of a cdm reference.
name

Name of the table to insert.
table

Table to insert to the cdm.
overwrite

Whether to overwrite an existent table.
temporary

Whether to create a temporary table.

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

isResultSuppressed

Description

isResultSuppressed

Usage

isResultSuppressed(result, minCellCount = 5)

Arguments

result

The suppressed result to check
minCellCount

Minimum count of records used when suppressing

Value

Warning or message with check result

Check if a table is empty or not

Description

Check if a table is empty or not

Usage

isTableEmpty(table)

Arguments

table

a table

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

cdm

A cdm reference or the source of a cdm reference.

Value

A character vector with the names of tables.

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

table

A cdm_table.
version

version of the cdm.
cast

Whether to cast columns to the correct type.

Value

An achilles_table object

cdm_reference objects constructor

Description

cdm_reference objects constructor

Usage

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

Arguments

tables

List of tables that are part of the OMOP Common Data Model reference.
cdmName

Name of the cdm object.
cdmVersion

Version of the cdm. Supported versions 5.3 and 5.4.
.softValidation

Whether to perform a soft validation of consistency. If set to FALSE, non overlapping observation periods are ensured.

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

src

Source to a cdm object.
sourceType

Type of the source object.

Value

A validated cdm source object.

Create an cdm table.

Description

Create an cdm table.

Usage

newCdmTable(table, src, name)

Arguments

table

A table that is part of a cdm.
src

The source of the table.
name

The name of the table.

Value

A cdm_table object

'codelist' object constructor

Description

'codelist' object constructor

Usage

newCodelist(x)

Arguments

x

A named list where each element contains a vector of concept IDs.

Value

A codelist object.

'codelist' object constructor

Description

'codelist' object constructor

Usage

newCodelistWithDetails(x)

Arguments

x

A named list where each element contains a tibble with the column concept_id

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

table

cdm_table object with at least: cohort_definition_id, subject_id, cohort_start_date, cohort_end_date.
cohortSetRef

Table with at least: cohort_definition_id, cohort_name
cohortAttritionRef

Table with at least: cohort_definition_id, number_subjects, number_records, reason_id, reason, excluded_subjects, excluded_records.
cohortCodelistRef

Table with at least: cohort_definition_id, codelist_name, and concept_id.
.softValidation

Whether to perform a soft validation of consistency. If set to FALSE four additional checks will be performed: 1) a check that cohort end date is not before cohort start date, 2) a check that there are no missing values in required columns, 3) a check that cohort duration is all within observation period, and 4) that there are no overlapping cohort entries

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

x

a named list of tibbles, each of which containing concept set definitions

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

table

A cdm_table.
version

version of the cdm.
cast

Whether to cast columns to the correct type.

Value

An omop_table object

'summarised_results' object constructor

Description

'summarised_results' object constructor

Usage

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

Arguments

x

Table.
settings

Settings for the summarised_result object.

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)

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

table

Table to see required columns.
field

Name of the specific field.
version

Version of the OMOP Common Data Model.
onlyRequired

deprecated

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

cdmVersion

cdm version of the omop cdm.

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

version

Version of the OMOP Common Data Model.

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

result

A ⁠<summarised_result>⁠.
pivotEstimatesBy

Names from which pivot wider the estimate values. If NULL the table will not be pivotted.
nameStyle

Name style (glue package specifications) to customise names when pivotting estimates. If NULL standard tidyr::pivot_wider formatting will be used.

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

x

A cdm_reference object
...

Included for compatibility with generic. Not used.

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

x

A codelist
...

Included for compatibility with generic. Not used.

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

x

A codelist with details
...

Included for compatibility with generic. Not used.

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

x

A concept set expression
...

Included for compatibility with generic. Not used.

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

cdm

A cdm reference.
name

Name of a table to read in the cdm_source space.

Value

A cdm_reference with new table.

Update cohort attrition.

Description

Update cohort attrition.

Usage

recordCohortAttrition(cohort, reason, cohortId = NULL)

Arguments

cohort

A cohort_table object.
reason

A character string.
cohortId

Cohort definition id of the cohort to update attrition. If NULL all cohort_definition_id are updated.

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)

Required columns that the result tables must have.

Description

Required columns that the result tables must have.

Usage

resultColumns(table = "summarised_result")

Arguments

table

Table to see required columns.

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

result

a summarised results object

Value

a summarised results object

Get settings from an object.

Description

Get settings from an object.

Usage

settings(x)

Arguments

x

Object

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

x

A cohort_table object.

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

x

A summarised_result object.

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

result

A ⁠<summarised_result>⁠.
metadata

Whether to include metadata columns in settings or not.

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

x

Object to know the source type.

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

result

A dataframe with at least the columns additional_name and additional_level.
keep

Whether to keep the original group_name and group_level columns.
fill

Optionally, a character that specifies what value should be filled in with when missing.

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

result

A data.frame.
keep

Whether to keep the original name-level columns.
fill

A character that specifies what value should be filled in when missing.
exclude

Name of a column pair to exclude.

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

result

A dataframe with at least the columns group_name and group_level.
keep

Whether to keep the original group_name and group_level columns.
fill

Optionally, a character that specifies what value should be filled in with when missing.

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

result

A dataframe with at least the columns strata_name and strata_level.
keep

Whether to keep the original group_name and group_level columns.
fill

Optionally, a character that specifies what value should be filled in with when missing.

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()
}

Identify variables in strata_name column

Description

Identifies and returns the unique values in strata_name column.

Usage

strataColumns(result)

Arguments

result

A tibble.

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()
}

Summary a cdm reference

Description

Summary a cdm reference

Usage

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

Arguments

object

A cdm reference object.
...

For compatibility (not used).

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)

Summary a generated cohort set

Description

Summary a generated cohort set

Usage

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

Arguments

object

A generated cohort set object.
...

For compatibility (not used).

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

object

A summarised_result object.
...

For compatibility (not used).

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

result

Result object
minCellCount

Minimum count of records to report results.

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

result

summarised_result object.
minCellCount

Minimum count of records to report results.

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

table

A cdm_table.

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

table

A cdm_table.

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

[Experimental] 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

x

A ⁠<summarised_result>⁠.
...

For compatibility (not used).

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

result

A ⁠<summarised_result>⁠.

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

x

Character vector to convert

Value

A snake_case vector

Examples

toSnakeCase("myVariable")

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

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

n

Number of identifiers.
exclude

Columns to exclude.
nChar

Number of characters.
prefix

A prefix for the identifiers.

Value

A character vector with n unique identifiers.

Create a unique table name

Description

Create a unique table name

Usage

uniqueTableName(prefix = "")

Arguments

prefix

Prefix for the table names.

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

x

Tibble or dataframe.
cols

Columns to aggregate.
keep

Whether to keep the original columns.
ignore

Level values to ignore.

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

x

Tibble or dataframe.
cols

Columns to aggregate.
keep

Whether to keep the original columns.
ignore

Level values to ignore.

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

x

Tibble or dataframe.
cols

Columns to aggregate.
keep

Whether to keep the original columns.
ignore

Level values to ignore.

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"))

validateAgeGroupArgument

Description

validateAgeGroupArgument

Usage

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

Arguments

ageGroup

age group in a list.
multipleAgeGroup

allow mutliple age group.
overlap

allow overlapping ageGroup.
null

null age group allowed true or false.
ageGroupName

Name of the default age group.
call

parent frame.

Value

validate ageGroup

validateCdmArgument

Description

validateCdmArgument

Usage

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

Arguments

cdm

A cdm_reference object
checkOverlapObservation

TRUE to perform check on no overlap observation period
checkStartBeforeEndObservation

TRUE to perform check on correct observational start and end date
checkPlausibleObservationDates

TRUE to perform check that there are no implausible observation period start dates (before 1800-01-01) or end dates (after the current date)
checkPerson

TRUE to perform check on person id in all clincial table are in person table
validation

How to perform validation: "error", "warning".
call

A call argument to pass to cli functions.

Value

A cdm_reference object

Validate a cohort table input.

Description

Validate a cohort table input.

Usage

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

Arguments

cohort

Object to be validated as a valid cohort input.
checkEndAfterStart

If TRUE a check that all cohort end dates come on or after cohort start date will be performed.
checkOverlappingEntries

If TRUE a check that no individuals have overlapping cohort entries will be performed.
checkMissingValues

If TRUE a check that there are no missing values in required fields will be performed.
checkInObservation

If TRUE a check that cohort entries are within the individuals observation periods will be performed.
dropExtraColumns

Whether to drop extra columns that are not the required ones.
validation

How to perform validation: "error", "warning".
call

A call argument to pass to cli functions.

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,
  validation = "error",
  call = parent.frame()
)

Arguments

cohortId

A cohortId vector to be validated.
cohort

A cohort_table object.
validation

How to perform validation: "error", "warning".
call

A call argument to pass to cli functions.

Validate conceptSet argument.

Description

Validate conceptSet argument.

Usage

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

Arguments

conceptSet

It can be either a named list of concepts or a codelist, codelist_with_details or conceptSetExpression object.
cdm

A cdm_reference object, needed if a conceptSetExpression is provided.
validation

How to perform validation: "error", "warning".
call

A call argument to pass to cli functions.

Validate name argument.

Description

Validate name argument.

Usage

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

Arguments

name

Name of a new table to be added to a cdm object.
cdm

A cdm_reference object. It will check if a table named name already exists in the cdm.
validation

How to perform validation: "error", "warning".
null

If TRUE, name can be NULL
call

A call argument to pass to cli functions.

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

x

A tibble.
prefix

Prefix for the name-level pair, e.g. 'strata' for strata_name-strata_level pair.
sep

Separation pattern.
validation

Either 'error', 'warning' or 'message'.
call

Will be used by cli to report errors.

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

table

The table to check if the column already exists.
column

Character vector with the name(s) of the new column(s).
validation

Whether to throw warning or error.
call

Passed to cli functions.

Value

table without conflicting columns.

validateResultArgument

Description

validateResultArgument

Usage

validateResultArguemnt(result, validation = "error", call = parent.frame())

Arguments

result

summarise result object to validate
validation

message to return
call

parent.frame

Value

summarise result object

validateResultArgument

Description

validateResultArgument

Usage

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

Arguments

result

summarised_result object to validate.
checkNoDuplicates

Whether there are not allowed duplicates in the result object.
checkNameLevel

Whether the name-level paired columns are can be correctly split.
checkSuppression

Whether the suppression in the result object is well defined.
validation

Only error is supported at the moment.
call

parent.frame

Value

summarise result object

validateWindowArgument

Description

validateWindowArgument

Usage

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

Arguments

window

time window
snakeCase

return default window name in snake case if TRUE
call

A call argument to pass to cli functions.

Value

time window