Subset a cdm reference object.
Description
Subset a cdm reference object.
Usage
## S3 method for class 'cdm_reference' x[[name]]## 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"]]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## S3 replacement method for class 'cdm_reference' cdm[[name]] <- value
Arguments
cdm |
A |
name |
Name where the new table will be assigned. |
value |
Table with the same source as the |
Value
The cdm reference.
Subset a cdm reference object.
Description
Subset a cdm reference object.
Usage
## S3 method for class 'cdm_reference' x$name## S3 method for class 'cdm_reference' x$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$personlibrary(omopgenerics) library(dplyr, warn.conflicts = FALSE) cdm <- cdmFromTables( tables = list( "person" = tibble( person_id = c(1, 2, 3), gender_concept_id = 0, year_of_birth = 1990, race_concept_id = 0, ethnicity_concept_id = 0 ), "observation_period" = tibble( observation_period_id = 1:3, person_id = 1:3, observation_period_start_date = as.Date("2000-01-01"), observation_period_end_date = as.Date("2023-12-31"), period_type_concept_id = 0 ) ), cdmName = "mock" ) cdm$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$name <- value## S3 replacement method for class 'cdm_reference' cdm$name <- value
Arguments
cdm |
A |
name |
Name where the new table will be assigned. |
value |
Table with the same source as the |
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$personlibrary(omopgenerics) cdm <- cdmFromTables( tables = list( "person" = dplyr::tibble( person_id = c(1, 2, 3), gender_concept_id = 0, year_of_birth = 1990, race_concept_id = 0, ethnicity_concept_id = 0 ), "observation_period" = dplyr::tibble( observation_period_id = 1:3, person_id = 1:3, observation_period_start_date = as.Date("2000-01-01"), observation_period_end_date = as.Date("2023-12-31"), period_type_concept_id = 0 ) ), cdmName = "mock" ) cdm$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())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")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")achillesTables(version = "5.3")
Arguments
version |
Version of the OMOP Common Data Model. |
Value
Names of the tables that contain the results from the achilles analyses
Examples
library(omopgenerics) achillesTables()library(omopgenerics) achillesTables()
Identify variables in additional_name column
Description
Identifies and returns the unique values in additional_name column.
Usage
additionalColumns(result)additionalColumns(result)
Arguments
result |
A |
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() }{ 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))addSettings(result, settingsColumn = settingsColumns(result))
Arguments
result |
A |
settingsColumn |
Settings to be added as columns, by default
|
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() }{ 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 satisfies certain conditions.
Description
Assert that an object is a character and satisfies certain conditions.
Usage
assertCharacter( x, length = NULL, na = FALSE, null = FALSE, empty = TRUE, unique = FALSE, named = FALSE, minNumCharacter = 0, nm = deparse1(substitute(x), backtick = TRUE), call = parent.frame(), msg = NULL )assertCharacter( x, length = NULL, na = FALSE, null = FALSE, empty = TRUE, unique = FALSE, named = FALSE, minNumCharacter = 0, nm = deparse1(substitute(x), backtick = TRUE), call = parent.frame(), msg = NULL )
Arguments
x |
Variable to check. |
length |
Required length. If |
na |
Whether it can contain NA values. |
null |
Whether it can be NULL. |
empty |
Whether it can be empty. |
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. |
nm |
Name to use in error messages. Defaults to the expression supplied
to |
call |
Call argument that will be passed to |
msg |
Custom error message. |
Assert that an object is one of a set of options.
Description
Assert that an object is one of a set of options.
Usage
assertChoice( x, choices, length = NULL, na = FALSE, null = FALSE, empty = TRUE, unique = FALSE, named = FALSE, nm = deparse1(substitute(x), backtick = TRUE), call = parent.frame(), msg = NULL )assertChoice( x, choices, length = NULL, na = FALSE, null = FALSE, empty = TRUE, unique = FALSE, named = FALSE, nm = deparse1(substitute(x), backtick = TRUE), call = parent.frame(), msg = NULL )
Arguments
x |
Variable to check. |
choices |
Options that x is allowed to be. |
length |
Required length. If |
na |
Whether it can contain NA values. |
null |
Whether it can be NULL. |
empty |
Whether it can be empty. |
unique |
Whether it has to contain unique elements. |
named |
Whether it has to be named. |
nm |
Name to use in error messages. Defaults to the expression supplied
to |
call |
Call argument that will be passed to |
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, empty = TRUE, all = FALSE, extra = TRUE, nm = deparse1(substitute(x), backtick = TRUE), call = parent.frame(), msg = NULL )assertClass( x, class, length = NULL, null = FALSE, empty = TRUE, all = FALSE, extra = TRUE, nm = deparse1(substitute(x), backtick = TRUE), call = parent.frame(), msg = NULL )
Arguments
x |
Variable to check. |
class |
Expected class or classes. |
length |
Required length. If |
null |
Whether it can be NULL. |
empty |
Whether it can be empty. |
all |
Whether it should have all the classes or only at least one of them. |
extra |
Whether the object can have extra classes. |
nm |
Name to use in error messages. Defaults to the expression supplied
to |
call |
Call argument that will be passed to |
msg |
Custom error message. |
Assert Date
Description
Assert Date
Usage
assertDate( x, length = NULL, na = FALSE, null = FALSE, empty = TRUE, unique = FALSE, named = FALSE, nm = deparse1(substitute(x), backtick = TRUE), call = parent.frame(), msg = NULL )assertDate( x, length = NULL, na = FALSE, null = FALSE, empty = TRUE, unique = FALSE, named = FALSE, nm = deparse1(substitute(x), backtick = TRUE), call = parent.frame(), msg = NULL )
Arguments
x |
Expression to check. |
length |
Required length. If |
na |
Whether it can contain NA values. |
null |
Whether it can be NULL. |
empty |
Whether it can be empty. |
unique |
Whether it has to contain unique elements. |
named |
Whether it has to be named. |
nm |
Name to use in error messages. Defaults to the expression supplied
to |
call |
Call argument that will be passed to |
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, empty = TRUE, unique = FALSE, named = FALSE, class = NULL, nm = deparse1(substitute(x), backtick = TRUE), call = parent.frame(), msg = NULL )assertList( x, length = NULL, na = FALSE, null = FALSE, empty = TRUE, unique = FALSE, named = FALSE, class = NULL, nm = deparse1(substitute(x), backtick = TRUE), call = parent.frame(), msg = NULL )
Arguments
x |
Variable to check. |
length |
Required length. If |
na |
Whether it can contain NA values. |
null |
Whether it can be NULL. |
empty |
Whether it can be empty. |
unique |
Whether it has to contain unique elements. |
named |
Whether it has to be named. |
class |
Class that the elements must have. |
nm |
Name to use in error messages. Defaults to the expression supplied
to |
call |
Call argument that will be passed to |
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, empty = TRUE, unique = FALSE, named = FALSE, nm = deparse1(substitute(x), backtick = TRUE), call = parent.frame(), msg = NULL )assertLogical( x, length = NULL, na = FALSE, null = FALSE, empty = TRUE, unique = FALSE, named = FALSE, nm = deparse1(substitute(x), backtick = TRUE), call = parent.frame(), msg = NULL )
Arguments
x |
Variable to check. |
length |
Required length. If |
na |
Whether it can contain NA values. |
null |
Whether it can be NULL. |
empty |
Whether it can be empty. |
unique |
Whether it has to contain unique elements. |
named |
Whether it has to be named. |
nm |
Name to use in error messages. Defaults to the expression supplied
to |
call |
Call argument that will be passed to |
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, empty = TRUE, unique = FALSE, named = FALSE, nm = deparse1(substitute(x), backtick = TRUE), call = parent.frame(), msg = NULL )assertNumeric( x, integerish = FALSE, min = -Inf, max = Inf, length = NULL, na = FALSE, null = FALSE, empty = TRUE, unique = FALSE, named = FALSE, nm = deparse1(substitute(x), backtick = TRUE), 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 |
na |
Whether it can contain NA values. |
null |
Whether it can be NULL. |
empty |
Whether it can be empty. |
unique |
Whether it has to contain unique elements. |
named |
Whether it has to be named. |
nm |
Name to use in error messages. Defaults to the expression supplied
to |
call |
Call argument that will be passed to |
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, empty = TRUE, unique = FALSE, nm = deparse1(substitute(x), backtick = TRUE), call = parent.frame(), msg = NULL )assertTable( x, class = NULL, numberColumns = NULL, numberRows = NULL, columns = character(), allowExtraColumns = TRUE, null = FALSE, empty = TRUE, unique = FALSE, nm = deparse1(substitute(x), backtick = TRUE), call = parent.frame(), msg = NULL )
Arguments
x |
Variable to check. |
class |
A class that the table must have: "tbl", "data.frame", "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. |
empty |
Whether it can be empty. |
unique |
Whether it has to contain unique rows. |
nm |
Name to use in error messages. Defaults to the expression supplied
to |
call |
Call argument that will be passed to |
msg |
Custom error message. |
Assert that an expression is TRUE.
Description
Assert that an expression is TRUE.
Usage
assertTrue( x, null = FALSE, empty = TRUE, nm = deparse1(substitute(x), backtick = TRUE), call = parent.frame(), msg = NULL )assertTrue( x, null = FALSE, empty = TRUE, nm = deparse1(substitute(x), backtick = TRUE), call = parent.frame(), msg = NULL )
Arguments
x |
Expression to check. |
null |
Whether it can be NULL. |
empty |
Whether it can be empty. |
nm |
Name to use in error messages. Defaults to the expression supplied
to |
call |
Call argument that will be passed to |
msg |
Custom error message. |
Get attrition from an object.
Description
Get attrition from an object.
Usage
attrition(x)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)## 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$cohort1)library(omopgenerics) library(dplyr, warn.conflicts = FALSE) person <- tibble( person_id = 1, gender_concept_id = 0, year_of_birth = 1990, race_concept_id = 0, ethnicity_concept_id = 0 ) observation_period <- tibble( observation_period_id = 1, person_id = 1, observation_period_start_date = as.Date("2000-01-01"), observation_period_end_date = as.Date("2023-12-31"), period_type_concept_id = 0 ) cohort <- tibble( cohort_definition_id = c(1, 1, 1, 2), subject_id = 1, cohort_start_date = as.Date(c("2020-01-01", "2021-01-01", "2022-01-01", "2022-01-01")), cohort_end_date = as.Date(c("2020-01-01", "2021-01-01", "2022-01-01", "2022-01-01")), ) cdm <- cdmFromTables( tables = list("person" = person, "observation_period" = observation_period), cdmName = "my_example_cdm", cohortTables = list("cohort1" = cohort) ) attrition(cdm$cohort1)
Bind two or more objects of the same class.
Description
Bind two or more objects of the same class.
Usage
bind(...)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)## 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$cohort3) cdm$cohort3library(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(...)## 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$cohort1) mergedResult <- bind(result1, result2) mergedResultlibrary(omopgenerics) library(dplyr, warn.conflicts = FALSE) cdm <- cdmFromTables( tables = list( "person" = tibble( person_id = c(1, 2, 3), gender_concept_id = 0, year_of_birth = 1990, race_concept_id = 0, ethnicity_concept_id = 0 ), "observation_period" = tibble( observation_period_id = 1:3, person_id = 1:3, observation_period_start_date = as.Date("2000-01-01"), observation_period_end_date = as.Date("2023-12-31"), period_type_concept_id = 0 ) ), cdmName = "mock", cohortTables = list("cohort1" = tibble( cohort_definition_id = 1, subject_id = 1:3, cohort_start_date = as.Date("2010-01-01"), cohort_end_date = as.Date("2010-01-05") )) ) result1 <- summary(cdm) result2 <- summary(cdm$cohort1) mergedResult <- bind(result1, result2) mergedResult
Separate the cdm tables in classes
Description
Separate the cdm tables in classes
Usage
cdmClasses(cdm)cdmClasses(cdm)
Arguments
cdm |
A |
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, ...)cdmDisconnect(cdm, ...)
Arguments
cdm |
A |
... |
For compatibility; not used. |
Value
TRUE if the process was 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)cdmFromTables(tables, cdmName, cohortTables = list(), cdmVersion = NULL)
Arguments
tables |
List of tables that are part of the cdm object. |
cdmName |
Name of the |
cohortTables |
List of tables that contain cohort, cohort_set and cohort_attrition attributes. |
cdmVersion |
Version of the OMOP Common Data Model. Supported versions are "5.3" and "5.4". |
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" )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 or set the name of a cdm_reference associated object
Description
Get or set the name of a cdm_reference associated object
Usage
cdmName(x) cdmName(x) <- valuecdmName(x) cdmName(x) <- value
Arguments
x |
A cdm_reference or cdm_table object. |
value |
Name to assign to the cdm_reference 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$person) cdmName(cdm) <- "mock_2"library(omopgenerics) library(dplyr, warn.conflicts = FALSE) cdm <- cdmFromTables( tables = list( "person" = tibble( person_id = c(1, 2, 3), gender_concept_id = 0, year_of_birth = 1990, race_concept_id = 0, ethnicity_concept_id = 0 ), "observation_period" = tibble( observation_period_id = 1:3, person_id = 1:3, observation_period_start_date = as.Date("2000-01-01"), observation_period_end_date = as.Date("2023-12-31"), period_type_concept_id = 0 ) ), cdmName = "mock" ) cdmName(cdm) cdmName(cdm$person) cdmName(cdm) <- "mock_2"
Get the cdm_reference of a cdm_table.
Description
Get the cdm_reference of a cdm_table.
Usage
cdmReference(table)cdmReference(table)
Arguments
table |
A |
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$person)library(omopgenerics) library(dplyr, warn.conflicts = FALSE) cdm <- cdmFromTables( tables = list( "person" = tibble( person_id = c(1, 2, 3), gender_concept_id = 0, year_of_birth = 1990, race_concept_id = 0, ethnicity_concept_id = 0 ), "observation_period" = tibble( observation_period_id = 1:3, person_id = 1:3, observation_period_start_date = as.Date("2000-01-01"), observation_period_end_date = as.Date("2023-12-31"), period_type_concept_id = 0 ) ), cdmName = "mock" ) cdmReference(cdm$person)
Restrict the cdm object to a subset of tables.
Description
Restrict the cdm object to a subset of tables.
Usage
cdmSelect(cdm, ...)cdmSelect(cdm, ...)
Arguments
cdm |
A |
... |
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")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())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$person)library(omopgenerics) library(dplyr, warn.conflicts = FALSE) cdm <- cdmFromTables( tables = list( "person" = tibble( person_id = c(1, 2, 3), gender_concept_id = 0, year_of_birth = 1990, race_concept_id = 0, ethnicity_concept_id = 0 ), "observation_period" = tibble( observation_period_id = 1:3, person_id = 1:3, observation_period_start_date = as.Date("2000-01-01"), observation_period_end_date = as.Date("2023-12-31"), period_type_concept_id = 0 ) ), cdmName = "mock" ) cdmSource(cdm) cdmSource(cdm$person)
Get the source type of a cdm_reference object.
Description
![[Deprecated]](data:image/svg+xml;base64,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)
Usage
cdmSourceType(cdm)cdmSourceType(cdm)
Arguments
cdm |
A |
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)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)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)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$person)library(omopgenerics) library(dplyr, warn.conflicts = FALSE) cdm <- cdmFromTables( tables = list( "person" = tibble( person_id = c(1, 2, 3), gender_concept_id = 0, year_of_birth = 1990, race_concept_id = 0, ethnicity_concept_id = 0 ), "observation_period" = tibble( observation_period_id = 1:3, person_id = 1:3, observation_period_start_date = as.Date("2000-01-01"), observation_period_end_date = as.Date("2023-12-31"), period_type_concept_id = 0 ) ), cdmName = "mock" ) cdmVersion(cdm) cdmVersion(cdm$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() )checkCohortRequirements( cohort, checkEndAfterStart = TRUE, checkOverlappingEntries = TRUE, checkMissingValues = TRUE, checkInObservation = TRUE, type = "error", call = parent.frame() )
Arguments
cohort |
|
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( cohort, cohortId = NULL, codelistType = c("index event", "inclusion criteria", "exit criteria"), type = lifecycle::deprecated(), cohortTable = lifecycle::deprecated() )cohortCodelist( cohort, cohortId = NULL, codelistType = c("index event", "inclusion criteria", "exit criteria"), type = lifecycle::deprecated(), cohortTable = lifecycle::deprecated() )
Arguments
cohort |
A |
cohortId |
A particular cohort definition id that is present in the cohort table. If NULL the codelists of all cohorts will be retrieved. |
codelistType |
The reason for the codelist. Can be "index event", "inclusion criteria", or "exit criteria". |
type |
deprecated. |
cohortTable |
deprecated. |
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$cohort1, cohortId = 1, codelistType = "index event")library(omopgenerics) library(dplyr, warn.conflicts = FALSE) person <- tibble( person_id = 1, gender_concept_id = 0, year_of_birth = 1990, race_concept_id = 0, ethnicity_concept_id = 0 ) observation_period <- tibble( observation_period_id = 1, person_id = 1, observation_period_start_date = as.Date("2000-01-01"), observation_period_end_date = as.Date("2023-12-31"), period_type_concept_id = 0 ) cohort <- tibble( cohort_definition_id = c(1, 1, 1, 2), subject_id = 1, cohort_start_date = as.Date(c( "2020-01-01", "2021-01-01", "2022-01-01", "2022-01-01" )), cohort_end_date = as.Date(c( "2020-01-01", "2021-01-01", "2022-01-01", "2022-01-01" )) ) cdm <- cdmFromTables( tables = list("person" = person, "observation_period" = observation_period), cdmName = "my_example_cdm", cohortTables = list("cohort1" = cohort) ) cdm$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")cohortColumns(table, version = "5.3")
Arguments
table |
Either |
version |
Version of the OMOP Common Data Model. |
Value
Character vector with the column names
Required columns
Examples
library(omopgenerics) cohortColumns("cohort")library(omopgenerics) cohortColumns("cohort")
Get cohort counts from a cohort_table object.
Description
Get cohort counts from a cohort_table object.
Usage
cohortCount(cohort)cohortCount(cohort)
Arguments
cohort |
A |
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$cohort1)library(omopgenerics) library(dplyr, warn.conflicts = FALSE) person <- tibble( person_id = 1, gender_concept_id = 0, year_of_birth = 1990, race_concept_id = 0, ethnicity_concept_id = 0 ) observation_period <- tibble( observation_period_id = 1, person_id = 1, observation_period_start_date = as.Date("2000-01-01"), observation_period_end_date = as.Date("2023-12-31"), period_type_concept_id = 0 ) cohort <- tibble( cohort_definition_id = c(1, 1, 1, 2), subject_id = 1, cohort_start_date = as.Date(c( "2020-01-01", "2021-01-01", "2022-01-01", "2022-01-01" )), cohort_end_date = as.Date(c( "2020-01-01", "2021-01-01", "2022-01-01", "2022-01-01" )), ) cdm <- cdmFromTables( tables = list("person" = person, "observation_period" = observation_period), cdmName = "my_example_cdm", cohortTables = list("cohort1" = cohort) ) cohortCount(cdm$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")cohortTables(version = "5.3")
Arguments
version |
Version of the OMOP Common Data Model. |
Value
cohort tables
Examples
library(omopgenerics) cohortTables()library(omopgenerics) cohortTables()
Retrieve the cdm reference into a local cdm.
Description
Retrieve the cdm reference into a local cdm.
Usage
## S3 method for class 'cdm_reference' collect(x, ...)## S3 method for class 'cdm_reference' collect(x, ...)
Arguments
x |
A cdm_reference object. |
... |
For compatibility; 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)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, ...)## S3 method for class 'cohort_table' collect(x, ...)
Arguments
x |
|
... |
For compatibility; not used. |
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)combineStrata(levels, overall = FALSE)
Arguments
levels |
Vector of all strata levels to combine. |
overall |
Whether to provide an empty element |
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"))combineStrata(character()) combineStrata(character(), overall = TRUE) combineStrata(c("age", "sex"), overall = TRUE) combineStrata(c("age", "sex", "year"))
Compare the fields of two different OMOP CDM versions
Description
Compare the fields of two different OMOP CDM versions
Usage
compareOmopTableFields( cdmVersionReference = "5.3", cdmVersionComparator = "5.4" )compareOmopTableFields( cdmVersionReference = "5.3", cdmVersionComparator = "5.4" )
Arguments
cdmVersionReference |
An OMOP CDM version, it can either be "5.3" or "5.4". |
cdmVersionComparator |
An OMOP CDM version, it can either be "5.3" or "5.4". |
Value
A tibble with two columns: field and change.
Examples
library(omopgenerics) compareOmopTableFields("5.3", "5.4")library(omopgenerics) compareOmopTableFields("5.3", "5.4")
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, ... )## 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 an existing table. |
logPrefix |
Prefix to use when saving a log file. |
... |
For compatibility; not used. |
Value
Reference to a table in the cdm
Create the missing indexes
Description
![[Experimental]](data:image/svg+xml;base64,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)
Usage
createIndexes(x, name = NULL)createIndexes(x, name = NULL)
Arguments
x |
A |
name |
Name(s) of the CDM table(s). |
Value
Whether the process was completed successfully.
Create a log file
Description
Create a log file
Usage
createLogFile(logFile = here::here("log_{date}_{time}"))createLogFile(logFile = here::here("log_{date}_{time}"))
Arguments
logFile |
File path to write logging messages. You can use '{date}' and '{time}' to add the date and time in the log file name. |
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)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)createTableIndex(table, index)
Arguments
table |
A |
index |
Index to be created. |
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)dropSourceTable(cdm, name)
Arguments
cdm |
A |
name |
Name(s) of the table(s) to drop. 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)dropTable(cdm, name)
Arguments
cdm |
A |
name |
Name(s) of the table(s) to drop Tidyselect statements are supported. |
Value
The cdm reference.
Create an empty achilles table
Description
Create an empty achilles table
Usage
emptyAchillesTable(cdm, name)emptyAchillesTable(cdm, name)
Arguments
cdm |
A |
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")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)emptyCdmReference(cdmName, cdmVersion = NULL)
Arguments
cdmName |
Name of the |
cdmVersion |
Version of the OMOP Common Data Model. Supported versions are "5.3" and "5.4". |
Value
An empty cdm_reference
Examples
library(omopgenerics) emptyCdmReference(cdmName = "my_example_cdm")library(omopgenerics) emptyCdmReference(cdmName = "my_example_cdm")
Empty codelist object.
Description
Empty codelist object.
Usage
emptyCodelist()emptyCodelist()
Value
An empty codelist object.
Examples
emptyCodelist()emptyCodelist()
Empty codelist object.
Description
Empty codelist object.
Usage
emptyCodelistWithDetails()emptyCodelistWithDetails()
Value
An empty codelist object.
Examples
emptyCodelistWithDetails()emptyCodelistWithDetails()
Empty code search object
Description
Empty code search object
Usage
emptyCodeSearch(searchStrategy = NULL)emptyCodeSearch(searchStrategy = NULL)
Arguments
searchStrategy |
The search strategy used to generate the candidate codes. |
Value
An empty code_search object.
Create an empty cohort_table object
Description
Create an empty cohort_table object
Usage
emptyCohortTable(cdm, name, overwrite = TRUE)emptyCohortTable(cdm, name, overwrite = TRUE)
Arguments
cdm |
A |
name |
Name of the table to create. |
overwrite |
Whether to overwrite an existing 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)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)
Empty concept_set_expression object.
Description
Empty concept_set_expression object.
Usage
emptyConceptSetExpression()emptyConceptSetExpression()
Value
An empty concept_set_expression object.
Examples
emptyConceptSetExpression()emptyConceptSetExpression()
Create an empty omop table
Description
Create an empty omop table
Usage
emptyOmopTable(cdm, name)emptyOmopTable(cdm, name)
Arguments
cdm |
A |
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$drug_exposurelibrary(omopgenerics) person <- dplyr::tibble( person_id = 1, gender_concept_id = 0, year_of_birth = 1990, race_concept_id = 0, ethnicity_concept_id = 0 ) observation_period <- dplyr::tibble( observation_period_id = 1, person_id = 1, observation_period_start_date = as.Date("2000-01-01"), observation_period_end_date = as.Date("2023-12-31"), period_type_concept_id = 0 ) cdm <- cdmFromTables( tables = list("person" = person, "observation_period" = observation_period), cdmName = "test" ) cdm <- emptyOmopTable(cdm, "drug_exposure") cdm$drug_exposure
Empty summarised_result object.
Description
Empty summarised_result object.
Usage
emptySummarisedResult(settings = NULL)emptySummarisedResult(settings = NULL)
Arguments
settings |
Tibble/data.frame with the settings of the empty
summarised_result. It has to contain at least |
Value
An empty summarised_result object.
Examples
library(omopgenerics) emptySummarisedResult()library(omopgenerics) emptySummarisedResult()
Choices that can be present in estimate_type column.
Description
Choices that can be present in estimate_type column.
Usage
estimateTypeChoices()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()library(omopgenerics) estimateTypeChoices()
Existing indexes in a cdm object
Description
Usage
existingIndexes(x, name)existingIndexes(x, name)
Arguments
x |
A |
name |
Name(s) of the CDM table(s). |
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(x, name)expectedIndexes(x, name)
Arguments
x |
A |
name |
Name(s) of the CDM table(s). |
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")exportCodelist(x, path, type = "json")
Arguments
x |
A |
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 codelist with details object.
Description
Export a codelist with details object.
Usage
exportCodelistWithDetails(x, path, type = "json")exportCodelistWithDetails(x, path, type = "json")
Arguments
x |
A |
path |
Path to where files will be created. |
type |
Type of files to export. Currently 'json' and 'csv' are supported. |
Value
Files with codelists with details
Export a code_search object into an Excel spreadsheet
Description
Usage
exportCodeSearch(codeSearch, file, path = getwd())exportCodeSearch(codeSearch, file, path = getwd())
Arguments
codeSearch |
A |
file |
Name of the Excel spreadsheet to be created. |
path |
Path where to create the Excel file. |
Value
The code_search object is exported to a file.
Export a concept set expression.
Description
Export a concept set expression.
Usage
exportConceptSetExpression(x, path, type = "json")exportConceptSetExpression(x, path, type = "json")
Arguments
x |
A |
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(), logFile = getOption("omopgenerics.logFile"), logSqlPath = getOption("omopgenerics.log_sql_path"), logExplainPath = lifecycle::deprecated() )exportSummarisedResult( ..., minCellCount = 5, fileName = "results_{cdm_name}_{date}.csv", path = getwd(), logFile = getOption("omopgenerics.logFile"), logSqlPath = getOption("omopgenerics.log_sql_path"), logExplainPath = lifecycle::deprecated() )
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 the CSV file should be created. It is ignored if fileName is a full file name with a path included. |
logFile |
Path to the log file to export. |
logSqlPath |
Path to the folder that contains the sql logs to export. |
logExplainPath |
deprecated. |
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, ...)filterAdditional(result, ...)
Arguments
result |
A |
... |
Expressions that return a logical value ( |
Value
A <summarised_result> object with only the rows that satisfy the
specified additional columns.
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")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, ...)filterGroup(result, ...)
Arguments
result |
A |
... |
Expressions that return a logical value ( |
Value
A <summarised_result> object with only the rows that satisfy the
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")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> automatically
Description
Filter a <summarised_result> automatically
Usage
filterResult(result, ...)filterResult(result, ...)
Arguments
result |
A |
... |
Expressions that return a logical value. The columns used in the
expressions can be stored in the settings, strata, group, or additional
columns. |
Value
A <summarised_result> object with only the rows that satisfy the
specified filters.
Examples
library(dplyr) library(omopgenerics) x <- tibble( "result_id" = 1L, "cdm_name" = "eunomia", "group_name" = "cohort_name", "group_level" = "my_cohort", "strata_name" = "sex", "strata_level" = "Female", "variable_name" = "number subjects", "variable_level" = NA_character_, "estimate_name" = "count", "estimate_type" = "integer", "estimate_value" = "100", "additional_name" = "overall", "additional_level" = "overall" ) |> newSummarisedResult(settings = tibble( "result_id" = 1L, "analysis" = "overall" )) x |> filterResult(cohort_name == "my_cohort", sex == "Female")library(dplyr) library(omopgenerics) x <- tibble( "result_id" = 1L, "cdm_name" = "eunomia", "group_name" = "cohort_name", "group_level" = "my_cohort", "strata_name" = "sex", "strata_level" = "Female", "variable_name" = "number subjects", "variable_level" = NA_character_, "estimate_name" = "count", "estimate_type" = "integer", "estimate_value" = "100", "additional_name" = "overall", "additional_level" = "overall" ) |> newSummarisedResult(settings = tibble( "result_id" = 1L, "analysis" = "overall" )) x |> filterResult(cohort_name == "my_cohort", sex == "Female")
Filter a <summarised_result> using the settings
Description
Filter a <summarised_result> using the settings
Usage
filterSettings(result, ...)filterSettings(result, ...)
Arguments
result |
A |
... |
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
satisfy the 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")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, ...)filterStrata(result, ...)
Arguments
result |
A |
... |
Expressions that return a logical value ( |
Value
A <summarised_result> object with only the rows that satisfy the
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")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)getCohortId(cohort, cohortName = NULL)
Arguments
cohort |
A |
cohortName |
Names of the cohort of interest or a tidyselect expression referring to cohort names. 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)getCohortName(cohort, cohortId = NULL)
Arguments
cohort |
A |
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())getPersonIdentifier(x, call = parent.frame())
Arguments
x |
A table. |
call |
Call argument passed to |
Value
Person identifier column.
Identify variables in group_name column
Description
Identifies and returns the unique values in group_name column.
Usage
groupColumns(result)groupColumns(result)
Arguments
result |
A |
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() }{ 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 = NULL, recursive = FALSE)importCodelist(path, type = NULL, recursive = FALSE)
Arguments
path |
Path to a file or directory to import. |
type |
Type of files to import. If |
recursive |
If TRUE and path is a directory, search for files will recurse into directories. |
Value
A codelist
Import a codelist with details.
Description
Import a codelist with details.
Usage
importCodelistWithDetails(path, type = NULL, recursive = FALSE)importCodelistWithDetails(path, type = NULL, recursive = FALSE)
Arguments
path |
Path to a file or directory to import. |
type |
Type of files to import. If |
recursive |
If TRUE and path is a directory, search for files will recurse into directories. |
Value
A codelist_with_details object.
Import a code_search object from an Excel spreadsheet
Description
Usage
importCodeSearch(path)importCodeSearch(path)
Arguments
path |
Path to the Excel spreadsheet. |
Value
A code_search object.
Import a concept set expression.
Description
Import a concept set expression.
Usage
importConceptSetExpression(path, type = NULL, recursive = FALSE)importConceptSetExpression(path, type = NULL, recursive = FALSE)
Arguments
path |
Path to a file or directory to import. |
type |
Type of files to import. If |
recursive |
If TRUE and path is a directory, search for files will recurse into directories. |
Value
A concept set expression
Import a set of summarised results.
Description
Import a set of summarised results.
Usage
importSummarisedResult(path, recursive = FALSE, ...)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. |
... |
Passed to |
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)insertCdmTo(cdm, to)
Arguments
cdm |
A |
to |
A cdm_source or another cdm_reference, with a valid cdm_source. |
Details
If cdm is not local, it will be collected into memory before
insertion.
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)insertFromSource(cdm, value)
Arguments
cdm |
A |
value |
A table that shares source with the cdm_reference object. |
Value
A table in the cdm_reference environment
Insert a table into a cdm object.
Description
Insert a table into a cdm object.
Usage
insertTable(cdm, name, table, overwrite = TRUE, temporary = FALSE, ...)insertTable(cdm, name, table, overwrite = TRUE, temporary = FALSE, ...)
Arguments
cdm |
A |
name |
Name of the table to insert. |
table |
Table to insert into the cdm. |
overwrite |
Whether to overwrite an existing table. |
temporary |
Whether to create a temporary table. |
... |
For compatibility; not used. |
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)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
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)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)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 through a cdm object.
Description
List tables that can be accessed through a cdm object.
Usage
listSourceTables(cdm)listSourceTables(cdm)
Arguments
cdm |
A |
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") )logMessage( message = "Start logging file", logFile = getOption("omopgenerics.logFile") )
Arguments
message |
Message to log. |
logFile |
File path to write logging messages. Create a logFile with
|
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)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)newAchillesTable(table, version = "5.3", cast = FALSE)
Arguments
table |
A |
version |
Version of the OMOP Common Data Model. |
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)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 |
cdmVersion |
Version of the OMOP Common Data Model. Supported versions are "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") cdmlibrary(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)newCdmSource(src, sourceType)
Arguments
src |
Source for 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)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, cdm = NULL)newCodelist(x, cdm = NULL)
Arguments
x |
A named list where each element contains a vector of concept IDs. |
cdm |
A |
Value
A codelist object.
'codelist' object constructor
Description
'codelist' object constructor
Usage
newCodelistWithDetails(x, cdm = NULL)newCodelistWithDetails(x, cdm = NULL)
Arguments
x |
A named list where each element contains a tibble with the column concept_id |
cdm |
A |
Value
A codelist object.
Create a new code_search object
Description
Usage
newCodeSearch(codes, searchStrategy)newCodeSearch(codes, searchStrategy)
Arguments
codes |
A tibble with concept ids. It must contain "concept_id", "found_from", "concept_name", "vocabulary_version", "domain_id", "vocabulary_id", "concept_class_id", "standard_concept", "concept_code", "valid_start_date", "valid_end_date" and "invalid_reason" as columns. |
searchStrategy |
A tibble with the search strategy used to derive the codes. It must contain "strategy_id", "strategy_name" and "strategy_value" as columns. |
Value
A code_search 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 )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, concept_id and codelist_type. |
.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)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)
'concept_set_expression' object constructor
Description
'concept_set_expression' object constructor
Usage
newConceptSetExpression(x, cdm = NULL)newConceptSetExpression(x, cdm = NULL)
Arguments
x |
a named list of tibbles, each of which containing concept set definitions |
cdm |
A |
Value
A concept_set_expression
A new local source for the cdm
Description
A new local source for the cdm
Usage
newLocalSource()newLocalSource()
Value
A list in the format of a cdm source.
Examples
library(omopgenerics) newLocalSource()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)newOmopTable(table, version = "5.3", cast = FALSE)
Arguments
table |
A |
version |
Version of the OMOP Common Data Model. |
cast |
Whether to cast columns to the correct type. |
Value
An omop_table object
A new read-only source for the cdm
Description
This source can be used for cdm_reference objects that should allow reading
existing CDM tables, but should not allow creating, computing, inserting,
reading from, or dropping source tables.
Usage
newReadOnlySource()newReadOnlySource()
Value
A read-only source in the format of a cdm source.
Examples
library(omopgenerics) newReadOnlySource()library(omopgenerics) newReadOnlySource()
summarised_result object constructor
Description
summarised_result object constructor
Usage
newSummarisedResult(x, settings = attr(x, "settings"))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)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)numberRecords(x)
Arguments
x |
A cdm_table. |
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)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$observation_period)
Count the number of subjects that a cdm_table has.
Description
Count the number of subjects that a cdm_table has.
Usage
numberSubjects(x)numberSubjects(x)
Arguments
x |
A cdm_table. |
Value
An integer with the number of subjects in the table.
Examples
person <- dplyr::tibble( person_id = 1, gender_concept_id = 0, year_of_birth = 1990, race_concept_id = 0, ethnicity_concept_id = 0 ) observation_period <- dplyr::tibble( observation_period_id = 1, person_id = 1, observation_period_start_date = as.Date("2000-01-01"), observation_period_end_date = as.Date("2023-12-31"), period_type_concept_id = 0 ) cdm <- cdmFromTables( tables = list("person" = person, "observation_period" = observation_period), cdmName = "test" ) numberSubjects(cdm$observation_period)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() )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")library(omopgenerics) omopColumns("person")
Check or set the OMOP_DATA_FOLDER where the OMOP related data is stored.
Description
Check or set the OMOP_DATA_FOLDER where the OMOP related data is stored.
Usage
omopDataFolder(path = NULL)omopDataFolder(path = NULL)
Arguments
path |
Path to a folder to store the OMOP related data. If NULL the
current |
Value
The OMOP data folder.
Examples
omopDataFolder() omopDataFolder(file.path(tempdir(), "OMOP_DATA")) omopDataFolder()omopDataFolder() omopDataFolder(file.path(tempdir(), "OMOP_DATA")) omopDataFolder()
Return a table of omop cdm field information
Description
Return a table of omop cdm field information
Usage
omopTableFields(cdmVersion = "5.3")omopTableFields(cdmVersion = "5.3")
Arguments
cdmVersion |
cdm version of the omop cdm. |
Value
A tibble containing information 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")omopTables(version = "5.3")
Arguments
version |
Version of the OMOP Common Data Model. |
Value
Standard tables
Examples
library(omopgenerics) omopTables()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)pivotEstimates(result, pivotEstimatesBy = "estimate_name", nameStyle = NULL)
Arguments
result |
A |
pivotEstimatesBy |
Names from which to pivot the estimate values wider. If NULL the table will not be pivoted. |
nameStyle |
Name style (glue package specifications) to customise names when pivoting 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() }{ 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, ...)## S3 method for class 'cdm_reference' print(x, ...)
Arguments
x |
A cdm_reference object |
... |
For compatibility; 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)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 code search
Description
Print a code search
Usage
## S3 method for class 'code_search' print(x, ...)## S3 method for class 'code_search' print(x, ...)
Arguments
x |
A |
... |
For compatibility with the generic. |
Value
Invisibly returns the input.
Print a codelist
Description
Print a codelist
Usage
## S3 method for class 'codelist' print(x, ...)## S3 method for class 'codelist' print(x, ...)
Arguments
x |
A |
... |
For compatibility; 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)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, ...)## S3 method for class 'codelist_with_details' print(x, ...)
Arguments
x |
A |
... |
For compatibility; 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)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 'concept_set_expression' print(x, ...)## S3 method for class 'concept_set_expression' print(x, ...)
Arguments
x |
A |
... |
For compatibility; 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)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 the cdm.
Description
Read a table from the cdm_source and add it to the cdm.
Usage
readSourceTable(cdm, name)readSourceTable(cdm, name)
Arguments
cdm |
A |
name |
Name of a table to read in the cdm_source space. Tidyselect statements are supported. |
Value
A cdm_reference with new table.
Update cohort attrition.
Description
Update cohort attrition.
Usage
recordCohortAttrition(cohort, reason, cohortId = NULL)recordCohortAttrition(cohort, reason, cohortId = NULL)
Arguments
cohort |
A |
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$cohort1)library(omopgenerics) library(dplyr, warn.conflicts = FALSE) person <- tibble( person_id = 1, gender_concept_id = 0, year_of_birth = 1990, race_concept_id = 0, ethnicity_concept_id = 0 ) observation_period <- tibble( observation_period_id = 1, person_id = 1, observation_period_start_date = as.Date("2000-01-01"), observation_period_end_date = as.Date("2023-12-31"), period_type_concept_id = 0 ) cohort <- tibble( cohort_definition_id = c(1, 1, 1, 2), subject_id = 1, cohort_start_date = as.Date(c("2020-01-01", "2021-01-01", "2022-01-01", "2022-01-01")), cohort_end_date = as.Date(c("2020-01-01", "2021-01-01", "2022-01-01", "2022-01-01")), ) cdm <- cdmFromTables( tables = list("person" = person, "observation_period" = observation_period), cdmName = "my_example_cdm", cohortTables = list("cohort1" = cohort) ) cdm$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")resultColumns(table = "summarised_result")
Arguments
table |
Table to see required columns. |
Value
Required columns
Examples
library(omopgenerics) resultColumns()library(omopgenerics) resultColumns()
Check if different package versions are used for a summarised_result object
Description
Check if different package versions are used for a summarised_result object
Usage
resultPackageVersion(result)resultPackageVersion(result)
Arguments
result |
A summarised_result object. |
Value
A summarised_result object.
Get the result_type(s) defined in a certain package
Description
Get the result_type(s) defined in a certain package
Usage
resultType(pkg = NULL)resultType(pkg = NULL)
Arguments
pkg |
A character vector of the packages of interest |
Value
A named (package) list of the result_type registered in each package.
Examples
library(omopgenerics) resultType()library(omopgenerics) resultType()
Get the search strategy used to create a code_search
Description
Usage
searchStrategy(codeSearch)searchStrategy(codeSearch)
Arguments
codeSearch |
A |
Value
A search strategy tibble.
Get settings from an object.
Description
Get settings from an object.
Usage
settings(x)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)## 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$my_cohort)library(omopgenerics) library(dplyr, warn.conflicts = FALSE) person <- tibble( person_id = 1, gender_concept_id = 0, year_of_birth = 1990, race_concept_id = 0, ethnicity_concept_id = 0 ) observation_period <- tibble( observation_period_id = 1, person_id = 1, observation_period_start_date = as.Date("2000-01-01"), observation_period_end_date = as.Date("2023-12-31"), period_type_concept_id = 0 ) cohort <- tibble( cohort_definition_id = 1, subject_id = 1, cohort_start_date = as.Date("2010-01-01"), cohort_end_date = as.Date("2012-01-01") ) cdm <- cdmFromTables( tables = list("person" = person, "observation_period" = observation_period), cdmName = "test", cohortTables = list("my_cohort" = cohort) ) settings(cdm$my_cohort) cdm$my_cohort <- cdm$my_cohort |> newCohortTable(cohortSetRef = tibble( cohort_definition_id = 1, cohort_name = "new_name" )) settings(cdm$my_cohort)
Get settings from a summarised_result object.
Description
Get settings from a summarised_result object.
Usage
## S3 method for class 'summarised_result' settings(x)## S3 method for class 'summarised_result' settings(x)
Arguments
x |
A summarised_result object. |
Value
A table with the settings.
Examples
library(omopgenerics) library(dplyr, warn.conflicts = FALSE) person <- tibble( person_id = 1, gender_concept_id = 0, year_of_birth = 1990, race_concept_id = 0, ethnicity_concept_id = 0 ) observation_period <- tibble( observation_period_id = 1, person_id = 1, observation_period_start_date = as.Date("2000-01-01"), observation_period_end_date = as.Date("2023-12-31"), period_type_concept_id = 0 ) cohort <- tibble( cohort_definition_id = 1, subject_id = 1, cohort_start_date = as.Date("2010-01-01"), cohort_end_date = as.Date("2012-01-01") ) cdm <- cdmFromTables( tables = list("person" = person, "observation_period" = observation_period), cdmName = "test", cohortTables = list("my_cohort" = cohort) ) result <- summary(cdm$my_cohort) settings(result)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)settingsColumns(result, metadata = FALSE)
Arguments
result |
A |
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() }{ 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)sourceType(x)
Arguments
x |
Object for which to get 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")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() }{ 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")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() }{ 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")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() }{ 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")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() }{ 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(x, name = NULL)statusIndexes(x, name = NULL)
Arguments
x |
A |
name |
Name(s) of the CDM table(s). |
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)strataColumns(result)
Arguments
result |
A |
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() }{ 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" )summariseLogFile( logFile = getOption("omopgenerics.logFile"), cdmName = "unknown" )
Arguments
logFile |
File path to the log file to summarise. Create a logFile with
|
cdmName |
Name of the cdm for the |
Value
A summarised_result with the information from 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)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)
Summarise a cdm reference
Description
Summarise a cdm reference
Usage
## S3 method for class 'cdm_reference' summary(object, ...)## 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)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, ...)## S3 method for class 'cdm_source' summary(object, ...)
Arguments
object |
A generated cohort set object. |
... |
For compatibility; not used. |
Value
A list of properties of the cdm_source object.
Examples
summary(newLocalSource())summary(newLocalSource())
Summarise a generated cohort set
Description
Summarise a generated cohort set
Usage
## S3 method for class 'cohort_table' summary(object, ...)## S3 method for class 'cohort_table' summary(object, ...)
Arguments
object |
A cdm source 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)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)
Summarise a summarised_result
Description
Summarise a summarised_result
Usage
## S3 method for class 'summarised_result' summary(object, ...)## 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)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)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)## 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()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)tableName(table)
Arguments
table |
A |
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)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$person)
Get the table source of a cdm_table.
Description
Get the table source of a cdm_table.
Usage
tableSource(table)tableSource(table)
Arguments
table |
A |
Value
A cdm_source object.
Examples
library(omopgenerics) library(dplyr, warn.conflicts = FALSE) cdm <- cdmFromTables( tables = list( "person" = tibble( person_id = c(1, 2, 3), gender_concept_id = 0, year_of_birth = 1990, race_concept_id = 0, ethnicity_concept_id = 0 ), "observation_period" = tibble( observation_period_id = 1:3, person_id = 1:3, observation_period_start_date = as.Date("2000-01-01"), observation_period_end_date = as.Date("2023-12-31"), period_type_concept_id = 0 ) ), cdmName = "mock" ) tableSource(cdm$person)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 pivoted into columns using estimate_name as names, and group, strata,
and additional will be split.
Usage
## S3 method for class 'summarised_result' tidy(x, ...)## S3 method for class 'summarised_result' tidy(x, ...)
Arguments
x |
A |
... |
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() }{ 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)tidyColumns(result)
Arguments
result |
A |
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() }{ 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()tmpPrefix()
Value
A temporary prefix.
Examples
library(omopgenerics) tmpPrefix()library(omopgenerics) tmpPrefix()
Convert a character vector to snake case
Description
Convert a character vector to snake case
Usage
toSnakeCase(x)toSnakeCase(x)
Arguments
x |
Character vector to convert |
Value
A snake_case vector
Examples
toSnakeCase("myVariable") toSnakeCase(c("cohort1", "Cohort22b"))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() )transformToSummarisedResult( x, group = character(), strata = character(), additional = character(), estimates = character(), settings = character() )
Arguments
x |
A data.frame. |
group |
Columns in x to be used in group_name-group_level formatting. |
strata |
Columns in x to be used in strata_name-strata_level formatting. |
additional |
Columns in x to be used in additional_name-additional_level formatting. |
estimates |
Columns in x to be formatted into: estimate_name-estimate_type-estimate_value. |
settings |
Columns in x that form the settings of the <summarised_result> object. |
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") )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_")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 = "")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()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") )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"))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"))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"))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"))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"))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, empty = TRUE, nm = deparse1(substitute(table), backtick = TRUE), call = parent.frame() )validateAchillesTable( table, version = NULL, cast = FALSE, empty = TRUE, nm = deparse1(substitute(table), backtick = TRUE), call = parent.frame() )
Arguments
table |
A cdm_table to validate. |
version |
Version of the OMOP Common Data Model. |
cast |
Whether to cast columns to the correct type. |
empty |
Whether it can be empty. |
nm |
Name to use in error messages. Defaults to the expression supplied
to |
call |
Call argument passed to |
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, empty = TRUE, ageGroupName = "age_group", nm = deparse1(substitute(ageGroup), backtick = TRUE), call = parent.frame() )validateAgeGroupArgument( ageGroup, multipleAgeGroup = TRUE, overlap = FALSE, null = TRUE, empty = TRUE, ageGroupName = "age_group", nm = deparse1(substitute(ageGroup), backtick = TRUE), call = parent.frame() )
Arguments
ageGroup |
age group in a list. |
multipleAgeGroup |
Allow multiple age groups. |
overlap |
allow overlapping ageGroup. |
null |
null age group allowed true or false. |
empty |
Whether it can be empty. |
ageGroupName |
Name of the default age group. |
nm |
Name to use in error messages. Defaults to the expression supplied
to |
call |
Call argument passed to |
Value
validate ageGroup
Examples
validateAgeGroupArgument(list(c(0, 39), c(40, Inf)))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(), empty = TRUE, validation = "error", nm = deparse1(substitute(cdm), backtick = TRUE), call = parent.frame() )validateCdmArgument( cdm, checkOverlapObservation = FALSE, checkStartBeforeEndObservation = FALSE, checkPlausibleObservationDates = FALSE, checkPerson = FALSE, requiredTables = character(), empty = TRUE, validation = "error", nm = deparse1(substitute(cdm), backtick = TRUE), call = parent.frame() )
Arguments
cdm |
A |
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 clinical table are in person table |
requiredTables |
Name of tables that are required to be part of the cdm_reference object. |
empty |
Whether it can be empty. |
validation |
How to perform validation: "error", "warning". |
nm |
Name to use in error messages. Defaults to the expression supplied
to |
call |
Call argument passed to |
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)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, empty = TRUE, nm = deparse1(substitute(table), backtick = TRUE), call = parent.frame() )validateCdmTable( table, name = NULL, empty = TRUE, nm = deparse1(substitute(table), backtick = TRUE), call = parent.frame() )
Arguments
table |
Object to validate. |
name |
If we want to validate that the table has a specific name. |
empty |
Whether it can be empty. |
nm |
Name to use in error messages. Defaults to the expression supplied
to |
call |
Call argument passed to |
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, empty = TRUE, validation = "error", nm = deparse1(substitute(cohort), backtick = TRUE), call = parent.frame() )validateCohortArgument( cohort, checkEndAfterStart = FALSE, checkOverlappingEntries = FALSE, checkMissingValues = FALSE, checkInObservation = FALSE, checkAttributes = FALSE, checkPermanentTable = FALSE, dropExtraColumns = FALSE, empty = TRUE, validation = "error", nm = deparse1(substitute(cohort), backtick = TRUE), 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. |
checkAttributes |
Whether to check if attributes are present and populated correctly. |
checkPermanentTable |
Whether to check if the table has to be a permanent table. |
dropExtraColumns |
Whether to drop extra columns that are not the required ones. |
empty |
Whether it can be empty. |
validation |
How to perform validation: "error", "warning". |
nm |
Name to use in error messages. Defaults to the expression supplied
to |
call |
Call argument passed to |
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)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$cohort)
Validate cohortId argument. CohortId can either be a cohort_definition_id
value, a cohort_name or a tidyselect expression referring to cohort_names. If
you want to support tidyselect expressions please use the function as:
validateCohortIdArgument({{cohortId}}, cohort).
Description
Validate cohortId argument. CohortId can either be a cohort_definition_id
value, a cohort_name or a tidyselect expression referring to cohort_names. If
you want to support tidyselect expressions please use the function as:
validateCohortIdArgument({{cohortId}}, cohort).
Usage
validateCohortIdArgument( cohortId, cohort, null = TRUE, empty = TRUE, validation = "error", nm = deparse1(substitute(cohortId), backtick = TRUE), call = parent.frame() )validateCohortIdArgument( cohortId, cohort, null = TRUE, empty = TRUE, validation = "error", nm = deparse1(substitute(cohortId), backtick = TRUE), call = parent.frame() )
Arguments
cohortId |
A cohortId vector to be validated. |
cohort |
A |
null |
Whether |
empty |
Whether it can be empty. |
validation |
How to perform validation: "error", "warning". |
nm |
Name to use in error messages. Defaults to the expression supplied
to |
call |
Call argument passed to |
Examples
cdm <- cdmFromTables( tables = list( "person" = dplyr::tibble( person_id = c(1, 2, 3), gender_concept_id = 0, year_of_birth = 1990, race_concept_id = 0, ethnicity_concept_id = 0 ), "observation_period" = dplyr::tibble( observation_period_id = 1:3, person_id = 1:3, observation_period_start_date = as.Date("2000-01-01"), observation_period_end_date = as.Date("2023-12-31"), period_type_concept_id = 0 ) ), cohortTables = list( cohort = dplyr::tibble( cohort_definition_id = 1L, subject_id = 1L, cohort_start_date = as.Date("2020-01-01"), cohort_end_date = as.Date("2021-02-10") ) ), cdmName = "mock" ) validateCohortIdArgument(NULL, cdm$cohort) validateCohortIdArgument(1L, cdm$cohort) validateCohortIdArgument(2L, cdm$cohort, validation = "warning")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 existing column in a table.
Description
Validate whether a variable points to a certain existing column in a table.
Usage
validateColumn( column, x, type = c("character", "date", "logical", "numeric", "integer"), validation = "error", null = FALSE, empty = TRUE, nm = deparse1(substitute(column), backtick = TRUE), call = parent.frame() )validateColumn( column, x, type = c("character", "date", "logical", "numeric", "integer"), validation = "error", null = FALSE, empty = TRUE, nm = deparse1(substitute(column), backtick = TRUE), call = parent.frame() )
Arguments
column |
Name of a column that you want to check exists in |
x |
Table to check if the column exists. |
type |
Type of the column. |
validation |
How to perform validation: "error", "warning". |
null |
Whether |
empty |
Whether it can be empty. |
nm |
Name to use in error messages. Defaults to the expression supplied
to |
call |
Call argument passed to |
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")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 concept set expression 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 concept set expression or a codelist with details. The output will always be a codelist.
Usage
validateConceptSetArgument( conceptSet, cdm = NULL, empty = TRUE, validation = "error", nm = deparse1(substitute(conceptSet), backtick = TRUE), call = parent.frame() )validateConceptSetArgument( conceptSet, cdm = NULL, empty = TRUE, validation = "error", nm = deparse1(substitute(conceptSet), backtick = TRUE), call = parent.frame() )
Arguments
conceptSet |
It can be either a named list of concepts or a codelist, codelist_with_details or concept_set_expression object. |
cdm |
A |
empty |
Whether it can be empty. |
validation |
How to perform validation: "error", "warning". |
nm |
Name to use in error messages. Defaults to the expression supplied
to |
call |
Call argument passed to |
Value
A codelist object.
Examples
conceptSet <- list(disease_x = c(1L, 2L)) validateConceptSetArgument(conceptSet)conceptSet <- list(disease_x = c(1L, 2L)) validateConceptSetArgument(conceptSet)
Validate name argument. It must be a snake_case character vector. You can add
a cdm object to check that name is not already used in that cdm.
Description
Validate name argument. It must be a snake_case character vector. You can add
a cdm object to check that name is not already used in that cdm.
Usage
validateNameArgument( name, cdm = NULL, validation = "error", null = FALSE, empty = TRUE, nm = deparse1(substitute(name), backtick = TRUE), call = parent.frame() )validateNameArgument( name, cdm = NULL, validation = "error", null = FALSE, empty = TRUE, nm = deparse1(substitute(name), backtick = TRUE), call = parent.frame() )
Arguments
name |
Name of a new table to be added to a cdm object. |
cdm |
A |
validation |
How to perform validation: "error", "warning". |
null |
If TRUE, name can be NULL |
empty |
Whether it can be empty. |
nm |
Name to use in error messages. Defaults to the expression supplied
to |
call |
Call argument passed to |
Examples
# this is a validate name name <- "my_new_table" validateNameArgument(name) # this is not name <- "myTableNAME" validateNameArgument(name, validation = "warning")# 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, validation = "error", empty = TRUE, nm = deparse1(substitute(x), backtick = TRUE), call = parent.frame() )validateNameLevel( x, prefix, validation = "error", empty = TRUE, nm = deparse1(substitute(x), backtick = TRUE), call = parent.frame() )
Arguments
x |
A tibble. |
prefix |
Prefix for the name-level pair, e.g. 'strata' for strata_name-strata_level pair. |
validation |
Either 'error', 'warning' or 'message'. |
empty |
Whether it can be empty. |
nm |
Name to use in error messages. Defaults to the expression supplied
to |
call |
Will be used by cli to report errors. |
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, ..., empty = TRUE, nm = deparse1(substitute(nameStyle), backtick = TRUE), call = parent.frame() )validateNameStyle( nameStyle, ..., empty = TRUE, nm = deparse1(substitute(nameStyle), backtick = TRUE), call = parent.frame() )
Arguments
nameStyle |
A character vector. It must contain all the |
... |
Elements to be included. |
empty |
Whether it can be empty. |
nm |
Name to use in error messages. Defaults to the expression supplied
to |
call |
Call argument passed to |
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") )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, empty = TRUE, validation = "warning", nm = deparse1(substitute(column), backtick = TRUE), call = parent.frame() )validateNewColumn( table, column, empty = TRUE, validation = "warning", nm = deparse1(substitute(column), backtick = TRUE), 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). |
empty |
Whether it can be empty. |
validation |
How to perform validation: "error", "warning". |
nm |
Name to use in error messages. Defaults to the expression supplied
to |
call |
Call argument passed to |
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")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, empty = TRUE, nm = deparse1(substitute(omopTable), backtick = TRUE), call = parent.frame() )validateOmopTable( omopTable, version = NULL, cast = FALSE, empty = TRUE, nm = deparse1(substitute(omopTable), backtick = TRUE), call = parent.frame() )
Arguments
omopTable |
An omop_table to check. |
version |
Version of the OMOP Common Data Model. |
cast |
Whether to cast columns to the correct type. |
empty |
Whether it can be empty. |
nm |
Name to use in error messages. Defaults to the expression supplied
to |
call |
Call argument passed to |
Value
An omop_table object.
Validate whether an object is a valid 'summarised_result' object.
Description
Validate whether an object is a valid 'summarised_result' object.
Usage
validateResultArgument( result, checkNoDuplicates = FALSE, checkNameLevel = FALSE, checkSuppression = FALSE, empty = TRUE, validation = "error", nm = deparse1(substitute(result), backtick = TRUE), call = parent.frame() )validateResultArgument( result, checkNoDuplicates = FALSE, checkNameLevel = FALSE, checkSuppression = FALSE, empty = TRUE, validation = "error", nm = deparse1(substitute(result), backtick = TRUE), call = parent.frame() )
Arguments
result |
summarised_result object to validate. |
checkNoDuplicates |
Whether duplicates are not allowed in the result object. |
checkNameLevel |
Whether the name-level paired columns can be correctly split. |
checkSuppression |
Whether the suppression in the result object is well defined. |
empty |
Whether it can be empty. |
validation |
Only error is supported at the moment. |
nm |
Name to use in error messages. Defaults to the expression supplied
to |
call |
Call argument passed to |
Value
A summarised_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)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, empty = TRUE, nm = deparse1(substitute(strata), backtick = TRUE), call = parent.frame() )validateStrataArgument( strata, table, empty = TRUE, nm = deparse1(substitute(strata), backtick = TRUE), call = parent.frame() )
Arguments
strata |
A list of characters that point to columns in table. |
table |
A table with columns. |
empty |
Whether it can be empty. |
nm |
Name to use in error messages. Defaults to the expression supplied
to |
call |
Call argument passed to |
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)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 numeric, integerish by default, 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 numeric, integerish by default, and window start must be lower or equal than window end.
Usage
validateWindowArgument( window, snakeCase = TRUE, integerish = TRUE, empty = TRUE, nm = deparse1(substitute(window), backtick = TRUE), call = parent.frame() )validateWindowArgument( window, snakeCase = TRUE, integerish = TRUE, empty = TRUE, nm = deparse1(substitute(window), backtick = TRUE), call = parent.frame() )
Arguments
window |
time window |
snakeCase |
return default window name in snake case if TRUE |
integerish |
Whether window values must be integerish. |
empty |
Whether it can be empty. |
nm |
Name to use in error messages. Defaults to the expression supplied
to |
call |
Call argument passed to |
Value
time window
Examples
validateWindowArgument(list(c(0, 15), c(-Inf, Inf))) validateWindowArgument(list(c(0, 15), c(-Inf, Inf)), snakeCase = FALSE)validateWindowArgument(list(c(0, 15), c(-Inf, Inf))) validateWindowArgument(list(c(0, 15), c(-Inf, Inf)), snakeCase = FALSE)