Package 'metacore'

Title: A Centralized Metadata Object Focus on Clinical Trial Data Programming Workflows
Description: Create an immutable container holding metadata for the purpose of better enabling programming activities and functionality of other packages within the clinical programming workflow.
Authors: Christina Fillmore [aut, cre] , Maya Gans [aut] , Ashley Tarasiewicz [aut], Mike Stackhouse [aut] , Tamara Senior [aut], GSK/Atorus JPT [cph, fnd]
Maintainer: Christina Fillmore <[email protected]>
License: MIT + file LICENSE
Version: 0.1.3
Built: 2024-11-11 05:30:28 UTC
Source: https://github.com/atorus-research/metacore

Help Index

Check all data frames include the correct types of columns

Description

This function checks for vector types and accepted words

Usage

check_columns(
  ds_spec,
  ds_vars,
  var_spec,
  value_spec,
  derivations,
  codelist,
  supp
)

Arguments

ds_spec

dataset specification
ds_vars

dataset variables
var_spec

variable specification
value_spec

value specification
derivations

derivation information
codelist

codelist information
supp

supp information

Optional checks to consistency of metadata

Description

These functions check to see if values (e.g labels, formats) that should be consistent for a variable across all data are actually consistent.

Usage

check_inconsistent_labels(metacore)

check_inconsistent_types(metacore)

check_inconsistent_formats(metacore)

Arguments

metacore

metacore object to check

Value

If all variables are consistent it will return a message. If there are inconsistencies it will return a message and a dataset of the variables with inconsistencies.

Examples

## EXAMPLE WITH DUPLICATES
# Loads in a metacore obj called metacore
load(metacore_example("pilot_ADaM.rda"))
check_inconsistent_labels(metacore)

check_inconsistent_types(metacore)

## EXAMPLE WITHOUT DUPLICATES
# Loads in a metacore obj called metacore
load(metacore_example("pilot_SDTM.rda"))
check_inconsistent_labels(metacore)

check_inconsistent_formats(metacore)

check_inconsistent_types(metacore)

Column Validation Function

Description

Column Validation Function

Usage

check_structure(.data, col, func, any_na_acceptable, nm)

Arguments

.data

the dataframe to check the column for
col

the column to test
func

the function to use to assert column structure
any_na_acceptable

boolean, testing if the column can have missing
nm

name of column to check (for warning and error clarification)

Check Words in Column

Description

Check Words in Column

Usage

check_words(..., col)

Arguments

...

permissible words in the column
col

the column to check for specific words

Create table

Description

This function creates a table from excel sheets. This is mainly used internally for building spec readers, but is exported so others who need to build spec readers can use it.

Usage

create_tbl(doc, cols)

Arguments

doc

list of sheets from a excel doc
cols

vector of regex to get a datasets base on which columns it has. If the vector is named it will also rename the columns

Value

dataset (or list of datasets if not specific enough)

Define XML to DataDef Object

Description

Given a path, this function converts the define xml to a DataDef Object

Usage

define_to_metacore(path, quiet = FALSE)

Arguments

path

location of the define xml as a string
quiet

Option to quietly load in, this will suppress warnings, but not errors

Value

DataDef Object

Get Control Term

Description

Returns the control term (a vector for permitted values and a tibble for code lists) for a given variable. The dataset can be optionally specified if there is different control terminology for different datasets

Usage

get_control_term(metacode, variable, dataset = NULL)

Arguments

metacode

metacore object
variable

A variable name to get the controlled terms for. This can either be a string or just the name of the variable
dataset

A dataset name. This is not required if there is only one set of control terminology across all datasets

Value

a vector for permitted values and a 2-column tibble for codelists

Examples

## Not run: 
meta_ex <- spec_to_metacore(metacore_example("p21_mock.xlsx"))
get_control_term(meta_ex, QVAL, SUPPAE)
get_control_term(meta_ex, "QVAL", "SUPPAE")

## End(Not run)

Get Dataset Keys

Description

Returns the dataset keys for a given dataset

Usage

get_keys(metacode, dataset)

Arguments

metacode

metacore object
dataset

A dataset name

Value

a 2-column tibble with dataset key variables and key sequence

Examples

## Not run: 
meta_ex <- spec_to_metacore(metacore_example("p21_mock.xlsx"))
get_keys(meta_ex, "AE")
get_keys(meta_ex, AE)

## End(Not run)

Is metacore object

Description

Is metacore object

Usage

is_metacore(x)

Arguments

x

object to check

Value

TRUE if metacore, FALSE if not

Examples

# Loads in a metacore obj called metacore
load(metacore_example("pilot_ADaM.rda"))
is_metacore(metacore)

load metacore object

Description

load metacore object

Usage

load_metacore(path = NULL)

Arguments

path

location of the metacore object to load into memory

Value

metacore object in memory

R6 Class wrapper to create your own metacore object

Description

R6 Class wrapper to create your own metacore object

Usage

metacore(
  ds_spec = tibble(dataset = character(), structure = character(), label = character()),
  ds_vars = tibble(dataset = character(), variable = character(), keep = logical(),
    key_seq = integer(), order = integer(), core = character(), supp_flag = logical()),
  var_spec = tibble(variable = character(), label = character(), length = integer(), type
    = character(), common = character(), format = character()),
  value_spec = tibble(dataset = character(), variable = character(), where = character(),
    type = character(), sig_dig = integer(), code_id = character(), origin = character(),
    derivation_id = integer()),
  derivations = tibble(derivation_id = integer(), derivation = character()),
  codelist = tibble(code_id = character(), name = character(), type = character(), codes
    = list()),
  supp = tibble(dataset = character(), variable = character(), idvar = character(), qeval
    = character())
)

Arguments

ds_spec

contains each dataset in the study, with the labels for each
ds_vars

information on what variables are in each dataset + plus dataset specific variable information
var_spec

variable information that is shared across all datasets
value_spec

parameter specific information, as data is long the specs for wbc might be difference the hgb
derivations

contains derivation, it allows for different variables to have the same derivation
codelist

contains the code/decode information
supp

contains the idvar and qeval information for supplemental variables

Get path to metacore example

Description

metacore comes bundled with a number of sample files in its inst/extdata directory. This function make them easy to access. When testing or writing examples in other packages, it is best to use the 'pilot_ADaM.rda' example as it loads fastest.

Usage

metacore_example(file = NULL)

Arguments

file

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

Examples

metacore_example()
metacore_example("mock_spec.xlsx")

Select method to subset by a single dataframe

Description

Select method to subset by a single dataframe

Usage

MetaCore_filter(value)

Arguments

value

the dataframe to subset by

Read in all Sheets

Description

Given a path to a file, this function reads in all sheets of an excel file

Usage

read_all_sheets(path)

Arguments

path

string of the file path

Value

a list of datasets

save metacore object

Description

save metacore object

Usage

save_metacore(metacore_object, path = NULL)

Arguments

metacore_object

the metacore object in memory to save to disc
path

file path and file name to save metacore object

Value

an .rda file

Select metacore object to single dataset

Description

Select metacore object to single dataset

Usage

select_dataset(.data, dataset, simplify = FALSE)

Arguments

.data

the metacore object of dataframes
dataset

the specific dataset to subset by
simplify

return a single dataframe

Value

a filtered subset of the metacore object

Specification document to metacore object

Description

This function takes the location of an excel specification document and reads it in as a meta core object. At the moment it only supports specification in the format of pinnacle 21 specifications. But, the section level spec builder can be used as building blocks for bespoke specification documents.

Usage

spec_to_metacore(path, quiet = FALSE, where_sep_sheet = TRUE)

Arguments

path

string of file location
quiet

Option to quietly load in, this will suppress warnings, but not errors
where_sep_sheet

Option to tell if the where is in a separate sheet, like in older p21 specs or in a single sheet like newer p21 specs

Value

given a spec document it returns a metacore object

Check the type of spec document

Description

Check the type of spec document

Usage

spec_type(path)

Arguments

path

file location as a string

Value

returns string indicating the type of spec document

Spec to codelist

Description

Creates the value_spec from a list of datasets (optionally filtered by the sheet input). The named vector ⁠*_cols⁠ is used to determine which is the correct sheet and renames the columns.

Usage

spec_type_to_codelist(
  doc,
  codelist_cols = c(code_id = "ID", name = "[N|n]ame", code = "^[C|c]ode|^[T|t]erm",
    decode = "[D|d]ecode"),
  permitted_val_cols = NULL,
  dict_cols = c(code_id = "ID", name = "[N|n]ame", dictionary = "[D|d]ictionary", version
    = "[V|v]ersion"),
  sheets = NULL,
  simplify = FALSE
)

Arguments

doc

Named list of datasets @seealso read_all_sheets() for exact format
codelist_cols

Named vector of column names that make up the codelist. The column names can be regular expressions for more flexibility. But, the names must follow the given pattern
permitted_val_cols

Named vector of column names that make up the permitted value The column names can be regular expressions for more flexibility. This is optional, can be left as null if there isn't a permitted value sheet
dict_cols

Named vector of column names that make up the dictionary value The column names can be regular expressions for more flexibility. This is optional, can be left as null if there isn't a permitted value sheet
sheets

Optional, regular expressions of the sheets
simplify

Boolean value, if true will convert code/decode pairs that are all equal to a permitted value list. True by default

Value

a dataset formatted for the metacore object

See Also

Other spec builders: spec_type_to_derivations(), spec_type_to_ds_spec(), spec_type_to_ds_vars(), spec_type_to_value_spec(), spec_type_to_var_spec()

Spec to derivation

Description

Creates the derivation table from a list of datasets (optionally filtered by the sheet input). The named vector cols is used to determine which is the correct sheet and renames the columns. The derivation will be used for "derived" origins, the comments for "assigned" origins, and predecessor for "predecessor" origins.

Usage

spec_type_to_derivations(
  doc,
  cols = c(derivation_id = "ID", derivation = "[D|d]efinition|[D|d]escription"),
  sheet = "Method|Derivations?",
  var_cols = c(dataset = "[D|d]ataset|[D|d]omain", variable = "[N|n]ame|[V|v]ariables?",
    origin = "[O|o]rigin", predecessor = "[P|p]redecessor", comment = "[C|c]omment")
)

Arguments

doc

Named list of datasets @seealso read_all_sheets() for exact format
cols

Named vector of column names. The column names can be regular expressions for more flexibility. But, the names must follow the given pattern
sheet

Regular expression for the sheet name
var_cols

Named vector of the name(s) of the origin, predecessor and comment columns. These do not have to be on the specified sheet.

Value

a dataset formatted for the metacore object

See Also

Other spec builders: spec_type_to_codelist(), spec_type_to_ds_spec(), spec_type_to_ds_vars(), spec_type_to_value_spec(), spec_type_to_var_spec()

Spec to ds_spec

Description

Creates the ds_spec from a list of datasets (optionally filtered by the sheet input). The named vector cols is used to determine which is the correct sheet and renames the columns

Usage

spec_type_to_ds_spec(
  doc,
  cols = c(dataset = "[N|n]ame|[D|d]ataset|[D|d]omain", structure = "[S|s]tructure",
    label = "[L|l]abel|[D|d]escription"),
  sheet = NULL
)

Arguments

doc

Named list of datasets @seealso read_all_sheets() for exact format
cols

Named vector of column names. The column names can be regular expressions for more flexibility. But, the names must follow the given pattern
sheet

Regular expression for the sheet name

Value

a dataset formatted for the metacore object

See Also

Other spec builders: spec_type_to_codelist(), spec_type_to_derivations(), spec_type_to_ds_vars(), spec_type_to_value_spec(), spec_type_to_var_spec()

Spec to ds_vars

Description

Creates the ds_vars from a list of datasets (optionally filtered by the sheet input). The named vector cols is used to determine which is the correct sheet and renames the columns

Usage

spec_type_to_ds_vars(
  doc,
  cols = c(dataset = "[D|d]ataset|[D|d]omain", variable =
    "[V|v]ariable [[N|n]ame]?|[V|v]ariables?", order =
    "[V|v]ariable [O|o]rder|[O|o]rder", keep = "[K|k]eep|[M|m]andatory"),
  key_seq_sep_sheet = TRUE,
  key_seq_cols = c(dataset = "Dataset", key_seq = "Key Variables"),
  sheet = "[V|v]ar|Datasets"
)

Arguments

doc

Named list of datasets @seealso read_all_sheets() for exact format
cols

Named vector of column names. The column names can be regular expressions for more flexibility. But, the names must follow the given pattern
key_seq_sep_sheet

A boolean to indicate if the key sequence is on a separate sheet. If set to false add the key_seq column name to the cols vector.
key_seq_cols

names vector to get the key_sequence for each dataset
sheet

Regular expression for the sheet names

Value

a dataset formatted for the metacore object

See Also

Other spec builders: spec_type_to_codelist(), spec_type_to_derivations(), spec_type_to_ds_spec(), spec_type_to_value_spec(), spec_type_to_var_spec()

Spec to value_spec

Description

Creates the value_spec from a list of datasets (optionally filtered by the sheet input). The named vector cols is used to determine which is the correct sheet and renames the columns

Usage

spec_type_to_value_spec(
  doc,
  cols = c(dataset = "[D|d]ataset|[D|d]omain", variable = "[N|n]ame|[V|v]ariables?",
    origin = "[O|o]rigin", type = "[T|t]ype", code_id = "[C|c]odelist|Controlled Term",
    sig_dig = "[S|s]ignificant", where = "[W|w]here", derivation_id = "[M|m]ethod",
    predecessor = "[P|p]redecessor"),
  sheet = NULL,
  where_sep_sheet = TRUE,
  where_cols = c(id = "ID", where = c("Variable", "Comparator", "Value")),
  var_sheet = "[V|v]ar"
)

Arguments

doc

Named list of datasets @seealso read_all_sheets() for exact format
cols

Named vector of column names. The column names can be regular expressions for more flexibility. But, the names must follow the given pattern
sheet

Regular expression for the sheet name
where_sep_sheet

Boolean value to control if the where information in a separate dataset. If the where information is on a separate sheet, set to true and provide the column information with the where_cols inputs.
where_cols

Named list with an id and where field. All columns in the where field will be collapsed together
var_sheet

Name of sheet with the Variable information on it. Metacore expects each variable will have a row in the value_spec. Because many specification only have information in the value tab this is added. If the information already exists in the value tab of your specification set to NULL

Value

a dataset formatted for the metacore object

See Also

Other spec builders: spec_type_to_codelist(), spec_type_to_derivations(), spec_type_to_ds_spec(), spec_type_to_ds_vars(), spec_type_to_var_spec()

Spec to var_spec

Description

Creates the var_spec from a list of datasets (optionally filtered by the sheet input). The named vector cols is used to determine which is the correct sheet and renames the columns. (Note: the keep column will be converted logical)

Usage

spec_type_to_var_spec(
  doc,
  cols = c(variable = "[N|n]ame|[V|v]ariables?", length = "[L|l]ength", label =
    "[L|l]abel", type = "[T|t]ype", dataset = "[D|d]ataset|[D|d]omain", format =
    "[F|f]ormat"),
  sheet = "[V|v]ar"
)

Arguments

doc

Named list of datasets @seealso read_all_sheets() for exact format
cols

Named vector of column names. The column names can be regular expressions for more flexibility. But, the names must follow the given pattern
sheet

Regular expression for the sheet name

Value

a dataset formatted for the metacore object

See Also

Other spec builders: spec_type_to_codelist(), spec_type_to_derivations(), spec_type_to_ds_spec(), spec_type_to_ds_vars(), spec_type_to_value_spec()

XML to code list

Description

Reads in a define xml and creates a code_list table. The code_list table is a nested tibble where each row is a code list or permitted value list. The code column contains a vector of a tibble depending on if it is a permitted values or code list

Usage

xml_to_codelist(doc)

Arguments

doc

xml document

Value

a tibble containing the code list and permitted value information

See Also

Other xml builder: xml_to_derivations(), xml_to_ds_spec(), xml_to_ds_vars(), xml_to_value_spec(), xml_to_var_spec()

XML to derivation table

Description

This reads in a xml document and gets all the derivations/comments. These can be cross referenced to variables using the derivation_id's

Usage

xml_to_derivations(doc)

Arguments

doc

xml document

Value

dataframe with derivation id's and derivations

See Also

Other xml builder: xml_to_codelist(), xml_to_ds_spec(), xml_to_ds_vars(), xml_to_value_spec(), xml_to_var_spec()

XML to Data Set Spec

Description

Creates a dataset specification, which has the domain name and label for each dataset

Usage

xml_to_ds_spec(doc)

Arguments

doc

xml document

Value

data frame with the data set specifications

See Also

Other xml builder: xml_to_codelist(), xml_to_derivations(), xml_to_ds_vars(), xml_to_value_spec(), xml_to_var_spec()

XML to Data Set Var table

Description

Creates the ds_vars table, which acts as a key between the datasets and the var spec

Usage

xml_to_ds_vars(doc)

Arguments

doc

xml document

Value

data frame with the dataset and variables

See Also

Other xml builder: xml_to_codelist(), xml_to_derivations(), xml_to_ds_spec(), xml_to_value_spec(), xml_to_var_spec()

XML to value spec

Description

Takes a define xml and pulls out the value level metadata including codelist_id's, defines_id's, and where clause. There is one row per variable expect when there is a where clause, at which point there is one row per value.

Usage

xml_to_value_spec(doc)

Arguments

doc

xml document

Value

tibble with the value level information

See Also

Other xml builder: xml_to_codelist(), xml_to_derivations(), xml_to_ds_spec(), xml_to_ds_vars(), xml_to_var_spec()

XML to variable spec

Description

Takes a define xml and returns a dataset with specifications for each variable. The variable will just be the variable, unless the specification for that variable differ between datasets

Usage

xml_to_var_spec(doc)

Arguments

doc

define xml document

Value

data frame with variable, length, label columns

See Also

Other xml builder: xml_to_codelist(), xml_to_derivations(), xml_to_ds_spec(), xml_to_ds_vars(), xml_to_value_spec()