Help for package metatools

Type:

Package

Title:

Enable the Use of 'metacore' to Help Create and Check Dataset

Version:

0.2.0

Description:

Uses the metadata information stored in 'metacore' objects to check and build metadata associated columns.

License:

MIT + file LICENSE

URL:

https://github.com/pharmaverse/metatools, https://pharmaverse.github.io/metatools/

BugReports:

https://github.com/pharmaverse/metatools/issues

Depends:

R (≥ 4.1.0)

Imports:

cli, dplyr, lifecycle, magrittr, metacore (≥ 0.2.0), purrr, rlang, stringr, tibble, tidyr

Suggests:

covr, haven, pharmaversesdtm, safetyData, spelling, testthat (≥ 3.0.0)

Config/testthat/edition:

Encoding:

UTF-8

Language:

en-US

RoxygenNote:

7.3.2

NeedsCompilation:

Packaged:

2025-07-15 18:40:42 UTC; lfh66519

Author:

Liam Hobby [aut, cre], Christina Fillmore

[aut], Bill Denney [aut], Mike Stackhouse

[aut], Jana Stoilova [aut], Tamara Senior [aut], GlaxoSmithKline LLC [cph, fnd], F. Hoffmann-La Roche AG [cph, fnd], Atorus Research LLC [cph, fnd]

Maintainer:

Liam Hobby <liam.f.hobby@gsk.com>

Repository:

CRAN

Date/Publication:

2025-07-16 04:50:02 UTC

metatools: Enable the Use of 'metacore' to Help Create and Check Dataset

Description

Uses the metadata information stored in 'metacore' objects to check and build metadata associated columns.

Author(s)

Maintainer: Liam Hobby liam.f.hobby@gsk.com

Authors:

Christina Fillmore christina.e.fillmore@gsk.com (ORCID)
Bill Denney
Mike Stackhouse mike.stackhouse@atorusresearch.com (ORCID)
Jana Stoilova jana.stoilova@roche.com
Tamara Senior tamara.senior@roche.com

Other contributors:

GlaxoSmithKline LLC [copyright holder, funder]
F. Hoffmann-La Roche AG [copyright holder, funder]
Atorus Research LLC [copyright holder, funder]

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

lhs

A value or the magrittr placeholder.

rhs

A function call using the magrittr semantics.

Value

The result of calling rhs(lhs).

Apply labels to multiple variables on a data frame

Description

This function allows a user to apply several labels to a dataframe at once.

Usage

add_labels(data, ...)

Arguments

data

A data.frame or tibble

...

Named parameters in the form of variable = 'label'

Value

data with variable labels applied

Examples

add_labels(
    mtcars,
    mpg = "Miles Per Gallon",
    cyl = "Cylinders"
  )

Add Missing Variables

Description

This function adds in missing columns according to the type set in the metacore object. All values in the new columns will be missing, but typed correctly. If unable to recognize the type in the metacore object will return a logical type.

Usage

add_variables(dataset, metacore, dataset_name = deprecated())

Arguments

dataset

Dataset to add columns to. If all variables are present no columns will be added.

metacore

metacore object that only contains the specifications for the dataset of interest.

dataset_name

Optional string to specify the dataset. This is only needed if the metacore object provided hasn't already been subsetted.
Note: Deprecated in version 0.2.0. The dataset_name argument will be removed in a future release. Please use metacore::select_dataset to subset the metacore object to obtain metadata for a single dataset.

Value

The given dataset with any additional columns added

Examples

library(metacore)
library(haven)
library(dplyr)
load(metacore_example("pilot_ADaM.rda"))
spec <- metacore %>% select_dataset("ADSL")
data <- read_xpt(metatools_example("adsl.xpt")) %>%
   select(-TRTSDT, -TRT01P, -TRT01PN)
add_variables(data, spec)

Build a dataset from derived

Description

This function builds a dataset out of the columns that just need to be pulled through. So any variable that has a derivation in the format of 'dataset.variable' will be pulled through to create the new dataset. When there are multiple datasets present, they will be joined by the shared key_seq variables. These columns are often called 'Predecessors' in ADaM, but this is not universal so that is optional to specify.

Usage

build_from_derived(
  metacore,
  ds_list,
  dataset_name = deprecated(),
  predecessor_only = TRUE,
  keep = FALSE
)

Arguments

metacore

metacore object that contains the specifications for the dataset of interest.

ds_list

Named list of datasets that are needed to build the from. If the list is unnamed,then it will use the names of the objects.

dataset_name

Optional string to specify the dataset that is being built. This is only needed if the metacore object provided hasn't already been subsetted.
Note: Deprecated in version 0.2.0. The dataset_name argument will be removed in a future release. Please use metacore::select_dataset to subset the metacore object to obtain metadata for a single dataset.

predecessor_only

By default TRUE, so only variables with the origin of 'Predecessor' will be used. If FALSE any derivation matching the dataset.variable will be used.

keep

String to determine which columns from the original datasets should be kept

"FALSE" (default): only columns that are also present in the ADaM specification are kept in the output.
"ALL": all original columns are carried through to the ADaM, including those that have been renamed. e.g. if DM.ARM is a predecessor to DM.TRT01P, both ARM and TRT01P will be present as columns in the ADaM output.
"PREREQUISITE": columns are retained if they are required for future derivations in the specification. Additional prerequisite columns are identified as columns that appear in the 'derivation' column of the metacore object in the format "DATASET.VARIABLE", but not as direct predecessors. Predecessors are defined as columns where the derivation is a 1:1 copy of a column in a source dataset. e.g. derivation = "VS.VSTESTCD" is a predecessor, while derivation = "Value of VS.VSSTRESN where VS.VSTESTCD == 'Heart Rate'" contains both VS.VSTESTCD and VS.VSSTRESN as prerequisites, and these columns will be kept through to the ADaM.

Value

dataset

Examples

library(metacore)
library(haven)
library(magrittr)
load(metacore_example("pilot_ADaM.rda"))
spec <- metacore %>% select_dataset("ADSL")
ds_list <- list(DM = read_xpt(metatools_example("dm.xpt")))
build_from_derived(spec, ds_list, predecessor_only = FALSE)

Build the observations for a single QNAM

Description

Build the observations for a single QNAM

Usage

build_qnam(dataset, qnam, qlabel, idvar, qeval, qorig)

Arguments

dataset

Input dataset

qnam

QNAM value

qlabel

QLABEL value

idvar

IDVAR variable name (provided as a string)

qeval

QEVAL value to be populated for this QNAM

qorig

QORIG value to be populated for this QNAM

Value

Observations structured in SUPP format

Check Control Terminology for a Single Column

Description

This function checks the column in the dataset only contains the control terminology as defined by the metacore specification

Usage

check_ct_col(data, metacore, var, na_acceptable = NULL)

Arguments

data

Data to check

metacore

A metacore object to get the codelist from. If the variable has different codelists for different datasets the metacore object will need to be subsetted using select_dataset from the metacore package.

var

Name of variable to check

na_acceptable

Logical value, set to NULL by default, so the acceptability of missing values is based on if the core for the variable is "Required" in the metacore object. If set to TRUE then will pass check if values are in the control terminology or are missing. If set to FALSEthen NA will not be acceptable.

Value

Given data if column only contains control terms. If not, will error given the values which should not be in the column

Examples

library(metacore)
library(haven)
library(magrittr)
load(metacore_example("pilot_ADaM.rda"))
spec <- metacore %>% select_dataset("ADSL")
data <- read_xpt(metatools_example("adsl.xpt"))
check_ct_col(data, spec, TRT01PN)
check_ct_col(data, spec, "TRT01PN")

Check Control Terminology for a Dataset

Description

This function checks that all columns in the dataset only contains the control terminology as defined by the metacore specification

Usage

check_ct_data(data, metacore, na_acceptable = NULL, omit_vars = NULL)

Arguments

data

Dataset to check

metacore

metacore object that contains the specifications for the dataset of interest. If any variable has different codelists for different datasets the metacore object will need to be subsetted using select_dataset from the metacore package.

na_acceptable

logical value or character vector, set to NULL by default. NULL sets the acceptability of missing values based on if the core for the variable is "Required" in the metacore object. If set to TRUE then will pass check if values are in the control terminology or are missing. If set to FALSE then NA will not be acceptable. If set to a character vector then only the specified variables may contain NA values.

omit_vars

character vector indicating which variables should be skipped when doing the controlled terminology checks. Internally, omit_vars is evaluated before na_acceptable.

Value

Given data if all columns pass. It will error otherwise

Examples

library(haven)
library(metacore)
library(magrittr)
load(metacore_example("pilot_ADaM.rda"))
spec <- metacore %>% select_dataset("ADSL", quiet = TRUE)
data <- read_xpt(metatools_example("adsl.xpt"))

check_ct_data(data, spec, omit_vars = c("AGEGR2", "AGEGR2N"))
## Not run: 
# These examples produce errors:
check_ct_data(data, spec, na_acceptable = FALSE)
check_ct_data(data, spec, na_acceptable = FALSE, omit_vars = "DISCONFL")
check_ct_data(data, spec, na_acceptable = c("DSRAEFL", "DCSREAS"), omit_vars = "DISCONFL")

## End(Not run)

Check Uniqueness of Records by Key

Description

This function checks the uniqueness of records in the dataset by key using get_keys from the metacore package. If the key uniquely identifies each record the function will print a message stating everything is as expected. If records are not uniquely identified an error will explain the duplicates.

Usage

check_unique_keys(data, metacore, dataset_name = deprecated())

Arguments

data

Dataset to check

metacore

metacore object that only contains the specifications for the dataset of interest.

dataset_name

Value

message if the key uniquely identifies each dataset record, and error otherwise

Examples

library(haven)
library(metacore)
library(magrittr)
load(metacore_example("pilot_ADaM.rda"))
spec <- metacore %>% select_dataset("ADSL")
data <- read_xpt(metatools_example("adsl.xpt"))
check_unique_keys(data, spec)

Check Variable Names

Description

This function checks the variables in the dataset against the variables defined in the metacore specifications. If everything matches the function will print a message stating everything is as expected. If there are additional or missing variables an error will explain the discrepancies

Usage

check_variables(data, metacore, dataset_name = deprecated(), strict = TRUE)

Arguments

data

Dataset to check

metacore

metacore object that only contains the specifications for the dataset of interest.

dataset_name

strict

A logical value indicating whether to perform strict validation on the input dataset. If TRUE (default), errors will be raised if validation fails. If FALSE, warnings will be issued instead, allowing the function execution to continue event with invalid data.

Value

message if the dataset matches the specification and the dataset, and error otherwise

Examples

library(haven)
library(metacore)
library(magrittr)
load(metacore_example("pilot_ADaM.rda"))
spec <- metacore %>% select_dataset("ADSL")
data <- read_xpt(metatools_example("adsl.xpt"))
check_variables(data, spec)
data["DUMMY_COL"] <- NA
check_variables(data, spec, strict = FALSE)

Combine the Domain and Supplemental Qualifier

Description

Combine the Domain and Supplemental Qualifier

Usage

combine_supp(dataset, supp)

Arguments

dataset

Domain dataset

supp

Supplemental Qualifier dataset

Value

a dataset with the supp variables added to it

Examples

library(safetyData)
library(tibble)
combine_supp(sdtm_ae, sdtm_suppae)  %>% as_tibble()

Convert Variable to Factor with Levels Set by Control Terms

Description

This functions takes a dataset, a metacore object and a variable name. Then looks at the metacore object for the control terms for the given variable and uses that to convert the variable to a factor with those levels. If the control terminology is a code list, the code column will be used. The function fails if the control terminology is an external library

Usage

convert_var_to_fct(data, metacore, var)

Arguments

data

A dataset containing the variable to be modified

metacore

var

Name of variable to change

Value

Dataset with variable changed to a factor

Examples

library(metacore)
library(haven)
library(dplyr)
load(metacore_example("pilot_ADaM.rda"))
spec <- metacore %>% select_dataset("ADSL")
dm <- read_xpt(metatools_example("dm.xpt")) %>%
  select(USUBJID, SEX, ARM)
# Variable with codelist control terms
convert_var_to_fct(dm, spec, SEX)
# Variable with permitted value control terms
convert_var_to_fct(dm, spec, ARM)

Create Categorical Variable from Codelist

Description

Using the grouping from either the decode_var or code_var and a reference variable (ref_var) it will create a categorical variable and the numeric version of that categorical variable.

Usage

create_cat_var(
  data,
  metacore,
  ref_var,
  grp_var,
  num_grp_var = NULL,
  create_from_decode = FALSE,
  strict = TRUE
)

Arguments

data

Dataset with reference variable in it

metacore

ref_var

Name of variable to be used as the reference i.e AGE when creating AGEGR1

grp_var

Name of the new grouped variable

num_grp_var

Name of the new numeric decode for the grouped variable. This is optional if no value given no variable will be created

create_from_decode

Sets the decode column of the codelist as the column from which the variable will be created. By default the column is code.

strict

A logical value indicating whether to perform strict checking against the codelist. If TRUE will issue a warning if values in the ref_var column do not fit into the group definitions for the codelist in grp_var. If FALSE no warning is issued and values not defined by the codelist will likely result in NA results.

Value

dataset with new column added

Examples

library(metacore)
library(haven)
library(dplyr)
load(metacore_example("pilot_ADaM.rda"))
spec <- metacore %>% select_dataset("ADSL")
dm <- read_xpt(metatools_example("dm.xpt")) %>%
  select(USUBJID, AGE)
# Grouping Column Only
create_cat_var(dm, spec, AGE, AGEGR1)
# Grouping Column and Numeric Decode
create_cat_var(dm, spec, AGE, AGEGR1, AGEGR1N)

Create Subgroups

Description

Create Subgroups

Usage

create_subgrps(ref_vec, grp_defs, grp_labs = NULL)

Arguments

ref_vec

Vector of numeric values

grp_defs

Vector of strings with groupings defined. Format must be either: <00, >=00, 00-00, or 00-<00

grp_labs

Vector of strings with labels defined. The labels correspond to the associated grp_defs. i.e., "12-17" may translate to "12-17 years". If no grp_labs specified then grp_defs will be used.

Value

Character vector of the values in the subgroups

Examples

create_subgrps(c(1:10), c("<2", "2-5", ">5"))
create_subgrps(c(1:10), c("<=2", ">2-5", ">5"))
create_subgrps(c(1:10), c("<2", "2-<5", ">=5"))
create_subgrps(c(1:10), c("<2", "2-<5", ">=5"), c("<2 years", "2-5 years", ">=5 years"))

Create Variable from Codelist

Description

This functions uses code/decode pairs from a metacore object to create new variables in the data

Usage

create_var_from_codelist(
  data,
  metacore,
  input_var,
  out_var,
  codelist = NULL,
  decode_to_code = TRUE,
  strict = TRUE
)

Arguments

data

Dataset that contains the input variable

metacore

A metacore object to get the codelist from. This should be a subsetted metacore object (of subclass DatasetMeta) created using metacore::select_dataset.

input_var

Name of the variable that will be translated for the new column

out_var

Name of the output variable. Note: Unless a codelist is provided the grouping will always be from the code of the codelist associates with out_var.

codelist

Optional argument to supply a codelist. Must be a data.frame with code and decode columns such as those created by the function metacore::get_control_term. If no codelist is provided the codelist associated with the column supplied to out_var will be used. By default codelist is NULL.

decode_to_code

Direction of the translation. Default value is TRUE, i.e., assumes the input_var is the decode column of the codelist. Set to FALSE if the input_var is the code column of the codelist.

strict

A logical value indicating whether to perform strict checking against the codelist. If TRUE will issue a warning if values in the input_var column are not present in the codelist. If FALSE no warning is issued and values not present in the codelist will likely result in NA results.

Value

Dataset with a new column added

Examples

library(metacore)
library(tibble)
data <- tribble(
  ~USUBJID, ~VAR1, ~VAR2,
  1, "M", "Male",
  2, "F", "Female",
  3, "F", "Female",
  4, "U", "Unknown",
  5, "M", "Male",
)
spec <- spec_to_metacore(metacore_example("p21_mock.xlsx"), quiet = TRUE)
dm_spec <- select_dataset(spec, "DM", quiet = TRUE)
create_var_from_codelist(data, dm_spec, VAR2, SEX)
create_var_from_codelist(data, dm_spec, "VAR2", "SEX")
create_var_from_codelist(data, dm_spec, VAR1, SEX, decode_to_code = FALSE)

# Example providing a custom codelist
# This example also reverses the direction of translation
load(metacore_example('pilot_ADaM.rda'))
adlb_spec <- select_dataset(metacore, "ADLBC", quiet = TRUE)
adlb <- tibble(PARAMCD = c("ALB", "ALP", "ALT", "AST", "BILI", "BUN"))
create_var_from_codelist(
   adlb,
   adlb_spec,
   PARAMCD,
   PARAM,
   codelist = get_control_term(adlb_spec, PARAMCD),
   decode_to_code = FALSE,
   strict = FALSE)

## Not run: 
# Example expecting warning where `strict` == `TRUE`
adlb <- tibble(PARAMCD = c("ALB", "ALP", "ALT", "AST", "BILI", "BUN", "DUMMY1", "DUMMY2"))
create_var_from_codelist(
   adlb,
   adlb_spec,
   PARAMCD,
   PARAM,
   codelist = get_control_term(adlb_spec, PARAMCD),
   decode_to_code = FALSE,
   strict = TRUE)

## End(Not run)

Drop Unspecified Variables

Description

This function drops all unspecified variables. It will throw and error if the dataset does not contain all expected variables.

Usage

drop_unspec_vars(dataset, metacore, dataset_name = deprecated())

Arguments

dataset

Dataset to change

metacore

metacore object that only contains the specifications for the dataset of interest.

dataset_name

Value

Dataset with only specified columns

Examples

library(metacore)
library(haven)
library(dplyr)
load(metacore_example("pilot_ADaM.rda"))
spec <- metacore %>% select_dataset("ADSL")
data <- read_xpt(metatools_example("adsl.xpt")) %>%
  select(USUBJID, SITEID) %>%
  mutate(foo = "Hello")
drop_unspec_vars(data, spec)

Gets vector of control terminology which should be there

Description

This function checks the column in the dataset only contains the control terminology as defined by the metacore specification. It will return all values not found in the control terminology

Usage

get_bad_ct(data, metacore, var, na_acceptable = NULL)

Arguments

data

Data to check

metacore

var

Name of variable to check

na_acceptable

Value

vector

Examples

library(haven)
library(metacore)
library(magrittr)
load(metacore_example("pilot_ADaM.rda"))
spec <- metacore %>% select_dataset("ADSL")
data <- read_xpt(metatools_example("adsl.xpt"))
get_bad_ct(data, spec, "DCSREAS")
get_bad_ct(data, spec, "DCSREAS", na_acceptable = FALSE)

Make Supplemental Qualifier

Description

Make Supplemental Qualifier

Usage

make_supp_qual(dataset, metacore, dataset_name = deprecated())

Arguments

dataset

dataset the supp will be pulled from

metacore

A subsetted metacore object to get the supp information from. If not already subsetted then a dataset_name will need to be provided

dataset_name

Value

a CDISC formatted SUPP dataset

Examples


library(metacore)
library(safetyData)
library(tibble)
load(metacore_example("pilot_SDTM.rda"))
spec <- metacore %>% select_dataset("AE")
ae <- combine_supp(sdtm_ae, sdtm_suppae)
make_supp_qual(ae, spec) %>% as_tibble()

Get path to pkg example

Description

pkg comes bundled with a number of sample files in its inst/extdata directory. This function make them easy to access

Usage

metatools_example(file = NULL)

Arguments

file

Name of file. If NULL, the example files will be listed.

Examples

metatools_example()
metatools_example("dm.xpt")

Sort Columns by Order

Description

This function sorts the dataset according to the order found in the metacore object.

Usage

order_cols(data, metacore, dataset_name = deprecated())

Arguments

data

Dataset to sort

metacore

metacore object that contains the specifications for the dataset of interest.

dataset_name

Value

dataset with ordered columns

Examples

library(metacore)
library(haven)
library(magrittr)
load(metacore_example("pilot_ADaM.rda"))
spec <- metacore %>% select_dataset("ADSL")
data <- read_xpt(metatools_example("adsl.xpt"))
order_cols(data, spec)

Remove labels to multiple variables on a data frame

Description

This function allows a user to removes all labels to a dataframe at once.

Usage

remove_labels(data)

Arguments

data

A data.frame or tibble

Value

data with variable labels applied

Examples

library(haven)
data <- read_xpt(metatools_example("adsl.xpt"))
remove_labels(data)

Apply labels to a data frame using a metacore object

Description

This function leverages metadata available in a metacore object to apply labels to a data frame.

Usage

set_variable_labels(data, metacore, dataset_name = deprecated())

Arguments

data

A dataframe or tibble upon which labels will be applied

metacore

metacore object that contains the specifications for the dataset of interest.

dataset_name

Value

Dataframe with labels applied

Examples


mc <- metacore::spec_to_metacore(
        metacore::metacore_example("p21_mock.xlsx"),
        quiet=TRUE
        )
dm <- haven::read_xpt(metatools_example("dm.xpt"))
set_variable_labels(dm, mc, dataset_name = "DM")

Sort Rows by Key Sequence

Description

This function sorts the dataset according to the key sequence found in the metacore object.

Usage

sort_by_key(data, metacore, dataset_name = deprecated())

Arguments

data

Dataset to sort

metacore

metacore object that contains the specifications for the dataset of interest.

dataset_name

Value

dataset with ordered columns

Examples

library(metacore)
library(haven)
library(magrittr)
load(metacore_example("pilot_ADaM.rda"))
spec <- metacore %>% select_dataset("ADSL")
data <- read_xpt(metatools_example("adsl.xpt"))
sort_by_key(data, spec)

metatools: Enable the Use of 'metacore' to Help Create and Check Dataset

Description

Author(s)

See Also

Pipe operator

Description

Usage

Arguments

Value

Apply labels to multiple variables on a data frame

Description

Usage

Arguments

Value

Examples

Add Missing Variables

Description

Usage

Arguments

Value

Examples

Build a dataset from derived

Description

Usage

Arguments

Value

Examples

Build the observations for a single QNAM

Description

Usage

Arguments

Value

Check Control Terminology for a Single Column

Description

Usage

Arguments

Value

Examples

Check Control Terminology for a Dataset

Description

Usage

Arguments

Value

Examples

Check Uniqueness of Records by Key

Description

Usage

Arguments

Value

Examples

Check Variable Names

Description

Usage

Arguments

Value

Examples

Combine the Domain and Supplemental Qualifier

Description

Usage

Arguments

Value

Examples

Convert Variable to Factor with Levels Set by Control Terms

Description

Usage

Arguments

Value

Examples

Create Categorical Variable from Codelist

Description

Usage

Arguments

Value

Examples

Create Subgroups

Description

Usage

Arguments

Value

Examples