Help for package Rmonize

Type:

Package

Title:

Tools for Data Harmonization

Version:

2.0.0

Maintainer:

Guillaume Fabre <guijoseph.fabre@gmail.com>

Description:

Integrated tools to support rigorous and well documented data harmonization based on Maelstrom Research guidelines. The package includes functions to assess and prepare input elements, apply specified processing rules to generate harmonized datasets, validate data processing and identify processing errors, and document and summarize harmonized outputs. The harmonization process is defined and structured by two key user-generated documents: the DataSchema (specifying the list of harmonized variables to generate across datasets) and the Data Processing Elements (specifying the input elements and processing algorithms to generate harmonized variables in DataSchema formats). The package was developed to address key challenges of retrospective data harmonization in epidemiology (as described in Fortier I and al. (2017) <doi:10.1093/ije/dyw075>) but can be used for any data harmonization initiative.

License:

GPL-3

LazyData:

true

Depends:

R (≥ 3.5)

Imports:

dplyr (≥ 1.1.0), rlang, stringr, tidyr, crayon, haven, utils, fs, fabR (≥ 2.0.0), madshapR (≥ 2.0.0)

Suggests:

janitor, car, lubridate, knitr

URL:

https://github.com/maelstrom-research/Rmonize/

BugReports:

https://github.com/maelstrom-research/Rmonize/issues

RoxygenNote:

7.2.3

VignetteBuilder:

knitr

Encoding:

UTF-8

Language:

en-US

NeedsCompilation:

Packaged:

2025-06-30 18:30:38 UTC; guill

Author:

Guillaume Fabre

[aut, cre], Maelstrom Research [aut, fnd, cph]

Repository:

CRAN

Date/Publication:

2025-06-30 18:50:02 UTC

Rmonize: Tools for Data Harmonization

Description

Integrated tools to support rigorous and well documented data harmonization based on Maelstrom Research guidelines. The package includes functions to assess and prepare input elements, apply specified processing rules to generate harmonized datasets, validate data processing and identify processing errors, and document and summarize harmonized outputs. The harmonization process is defined and structured by two key user-generated documents: the DataSchema (specifying the list of harmonized variables to generate across datasets) and the Data Processing Elements (specifying the input elements and processing algorithms to generate harmonized variables in DataSchema formats). The package was developed to address key challenges of retrospective data harmonization in epidemiology (as described in Fortier I and al. (2017) doi:10.1093/ije/dyw075) but can be used for any data harmonization initiative.

Author(s)

Maintainer: Guillaume Fabre guijoseph.fabre@gmail.com (ORCID)

Authors:

Maelstrom Research info@maelstrom-research.org [funder, copyright holder]

Example objects to provide an illustrative use case

Description

Example input datasets, input data dictionaries, DataSchema, Data Processing Elements, harmonized output, and summary report.

Usage

Rmonize_examples

Format

`list`

A list with elements (data frames and lists) providing example objects for using the package:

original_dataset_study1: Example original dataset from .sav file for "study1"
original_dataset_study2: Example original dataset from .sav file for "study2"
original_dataset_study3: Example original dataset from .csv file for "study3"
original_dataset_study4: Example original dataset from .xlsx file for "study4"
original_dataset_study5: Example original dataset from .xlsx file for "study5"
original_data_dictionary_study4: Example original data dictionary from .xlsx file, for "study4"
original_data_dictionary_study5: Example original data dictionary from .xlsx file, for "study5"
input_dataset_study1: Example input dataset ready for processing, for "study1"
input_dataset_study2: Example input dataset ready for processing, for "study2"
input_dataset_study3: Example input dataset ready for processing, for "study3"
input_dataset_study4: Example input dataset ready for processing, for "study4"
input_dataset_study5: Example input dataset ready for processing, for "study5"
DataSchema: Example DataSchema
Data_Processing_Elements_no_errors: Example Data Processing Elements containing no errors
Data_Processing_Elements_with_errors: Example Data Processing Elements containing errors
harmonized_dossier: Example harmonized dossier
pooled_harmonized_dataset: Example pooled harmonized dataset
summary_report_harmonized_dossier: Example summary report of harmonized dossier

Examples

{

library(dplyr)
glimpse(Rmonize_examples$`DataSchema`)


}

Call to online documentation to download templates

Description

Direct call to online documentation to download templates.

Usage

Rmonize_templates()

Value

Nothing to be returned. The function opens a web page.

Examples

{

Rmonize_templates()

}

Call to package website

Description

Direct call to the package website, which includes an overview of the Rmonize process, vignettes and user guides, a reference list of functions and help pages, and package updates.

Usage

Rmonize_website()

Value

Nothing to be returned. The function opens a web page.

Examples

{

Rmonize_website()

}

Objects exported from other packages

Description

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

madshapR: as_data_dict

Validate and coerce as a Data Processing Elements object

Description

Checks if an object is a valid Data Processing Elements and returns it with the appropriate Rmonize::class attribute. This function mainly helps validate inputs within other functions of the package but could be used separately to ensure that an object has an appropriate structure.

Usage

as_data_proc_elem(object)

Arguments

object

A potential Data Processing Elements object to be coerced.

Details

The Data Processing Elements specifies the input elements and processing algorithms to generate harmonized variables in the DataSchema formats. It is also contains metadata used to generate documentation of the processing. A Data Processing Elements object is a data frame with specific columns used in data processing: dataschema_variable, input_dataset, input_variables, Mlstr_harmo::rule_category and Mlstr_harmo::algorithm. To initiate processing, the first entry must be the creation of a harmonized primary identifier variable (e.g., participant unique ID).

Value

A data frame with Rmonize::class 'data_proc_elem'.

Examples

{

# Use Rmonize_examples to run examples.
library(dplyr)

data_proc_elem <- as_data_proc_elem(Rmonize_examples$`Data_Processing_Elements_no_errors`)

head(data_proc_elem)

}

Validate and coerce as a DataSchema object

Description

Checks if an object is a valid DataSchema and returns it with the appropriate Rmonize::class attribute. This function mainly helps validate inputs within other functions of the package but could be used separately to ensure that an object has an appropriate structure.

Usage

as_dataschema(object, as_dataschema_mlstr = FALSE)

Arguments

object

A potential DataSchema object to be coerced.

as_dataschema_mlstr

Whether the output DataSchema should be coerced with specific format restrictions for compatibility with other Maelstrom Research software. FALSE by default.

Details

A DataSchema is the list of core variables to generate across datasets and related metadata. A DataSchema object is a list of data frames with elements named 'Variables' (required) and 'Categories' (if any). The 'Variables' element must contain at least the name column, and the 'Categories' element must contain at least the variable and name columns to be usable in any function. In 'Variables' the name column must also have unique entries, and in 'Categories' the combination of variable and name columns must also be unique.

The object may be specifically formatted to be compatible with additional Maelstrom Research software, in particular Opal environments.

Value

A list of data frame(s) named 'Variables' and (if any) 'Categories', with Rmonize::class 'dataschema'.

Examples

{

# Use Rmonize_examples to run examples.
library(dplyr)

dataschema <- as_dataschema(Rmonize_examples$`DataSchema`)
glimpse(dataschema)

}

Validate and coerce as a DataSchema object with specific format restrictions

Description

Checks if an object is a valid DataSchema with specific format restrictions for compatibility with other Maelstrom Research software and returns it with the appropriate Rmonize::class attribute. This function mainly helps validate inputs within other functions of the package but could be used separately to ensure that an object has an appropriate structure.

Usage

as_dataschema_mlstr(object)

Arguments

object

A potential DataSchema object to be coerced.

Details

The object may be specifically formatted to be compatible with additional Maelstrom Research software, in particular Opal environments.

Value

A list of data frame(s) named 'Variables' and (if any) 'Categories', with Rmonize::class 'dataschema_mlstr'.

Examples

{

# Use Rmonize_examples to run examples.
library(dplyr)

dataschema_mlstr <- as_dataschema_mlstr(Rmonize_examples$`DataSchema`)
glimpse(dataschema_mlstr)

}

Objects exported from other packages

Description

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

madshapR: as_dataset

Objects exported from other packages

Description

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

madshapR: as_dossier

Validate and coerce as a harmonized dossier object

Description

Checks if an object is a valid harmonized dossier and returns it with the appropriate Rmonize::class attribute. This function mainly helps validate inputs within other functions of the package but could be used separately to ensure that an object has an appropriate structure. The function has two arguments that can optionally be declared by the user (unique_col_dataset and unique_col_id). unique_col_dataset refers to the columns which contains name of each harmonized dataset. unique_col_id refers to the column in harmonized datasets which identifies unique combinations of observation/dataset. These two columns are added to ensure that there is always a unique entity identifier when datasets are pooled.

Usage

as_harmonized_dossier(
  object,
  dataschema = attributes(object)$`Rmonize::DataSchema`,
  data_proc_elem = attributes(object)$`Rmonize::Data Processing Elements`,
  harmonized_col_id = attributes(object)$`Rmonize::harmonized_col_id`,
  harmonized_col_dataset = attributes(object)$`Rmonize::harmonized_col_dataset`,
  harmonized_data_dict_apply = FALSE
)

Arguments

object

A A potential harmonized dossier object to be coerced.

dataschema

A DataSchema object.

data_proc_elem

A Data Processing Elements object.

harmonized_col_id

A character string identifying the name of the column present in every dataset to use as a participant identifier.

harmonized_col_dataset

A character string identifying the column to use for dataset names.

harmonized_data_dict_apply

Whether to apply the dataschema to each harmonized dataset. FALSE by default.

Details

A harmonized dossier is a named list containing one or more data frames, which are harmonized datasets. A harmonized dossier is generally the product of applying processing to a dossier object The name of each harmonized dataset (data frame) is taken from the reference input dataset. A harmonized dossier also contains the DataSchema and Data Processing Elements used in processing as attributes.

Value

A list of data frame(s) containing harmonized dataset(s). The DataSchema and Data Processing Elements are preserved as attributes of the harmonized dossier.

Examples

{

# Use Rmonize_examples to run examples.
 
library(dplyr)

harmonized_dossier <- Rmonize_examples[["harmonized_dossier"]]
harmonized_dossier <- as_harmonized_dossier(harmonized_dossier)

glimpse(harmonized_dossier$dataset_study1)

}

Objects exported from other packages

Description

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

madshapR: bookdown_open

Objects exported from other packages

Description

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

madshapR: data_dict_apply

Objects exported from other packages

Description

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

madshapR: data_dict_evaluate

Objects exported from other packages

Description

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

madshapR: data_dict_extract

Generate an assessment report for Data Processing Elements

Description

Assesses the content and structure of a Data Processing Elements object and generates reports of the results. This function can be used to evaluate data structure, presence of specific fields, coherence across elements, and data dictionary formats.

Usage

data_proc_elem_evaluate(data_proc_elem, taxonomy = NULL)

Arguments

data_proc_elem

A Data Processing Elements object.

taxonomy

An optional data frame identifying a variable classification schema.

Details

A taxonomy is a classification schema that can be defined for variable attributes. A taxonomy is usually extracted from an Opal environment, and a taxonomy object is a data frame that must contain at least the columns taxonomy, vocabulary, and terms. Additional details about Opal taxonomies are available online.

Value

A list of data frames containing assessment reports.

Examples

{
# Use Rmonize_examples to run examples.
library(dplyr)

data_proc_elem <- Rmonize_examples$`Data_Processing_Elements_no_errors`

glimpse(data_proc_elem)
}

Generate an assessment report for a DataSchema

Description

Assesses the content and structure of a DataSchema object and generates reports of the results. This function can be used to evaluate data structure, presence of specific fields, coherence across elements, and data dictionary formats.

Usage

dataschema_evaluate(dataschema, taxonomy = NULL)

Arguments

dataschema

A DataSchema object.

taxonomy

An optional data frame identifying a variable classification schema.

Details

Value

A list of data frames containing assessment reports.

Examples

{
# Use Rmonize_examples to run examples.
library(dplyr)

dataschema <- Rmonize_examples$`DataSchema`
eval_dataschema <- dataschema_evaluate(dataschema)

glimpse(eval_dataschema)

}

Generate a DataSchema based on Data Processing Elements

Description

Generates a DataSchema from a Data Processing Elements.

Usage

dataschema_extract(data_proc_elem)

Arguments

data_proc_elem

A Data Processing Elements object.

Details

Value

A list of data frame(s) named 'Variables' and (if any) 'Categories', with Rmonize::class 'dataschema'.

Examples

{

# Use Rmonize_examples to run examples.
library(dplyr)

dataschema <- dataschema_extract(Rmonize_examples$`Data_Processing_Elements_no_errors`)
glimpse(dataschema)

}

Objects exported from other packages

Description

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

madshapR: dataset_evaluate

Objects exported from other packages

Description

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

madshapR: dataset_summarize

Objects exported from other packages

Description

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

madshapR: dataset_visualize

Objects exported from other packages

Description

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

madshapR: dossier_create

Objects exported from other packages

Description

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

madshapR: dossier_evaluate

Objects exported from other packages

Description

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

madshapR: dossier_summarize

Generate harmonized dataset(s) and associated metadata

Description

Reads a DataSchema and Data Processing Elements to generate a harmonized dossier from input dataset(s) in a dossier and associated metadata. The function has one argument that can optionally be declared by the user (unique_col_dataset). It refers to the columns which contains name of each harmonized dataset. These two columns are added to ensure that there is always a unique entity identifier when datasets are pooled.

Usage

harmo_process(
  object,
  dataschema = attributes(object)$`Rmonize::DataSchema`,
  data_proc_elem = attributes(object)$`Rmonize::Data Processing Elements`,
  harmonized_col_dataset = attributes(object)$`Rmonize::harmonized_col_dataset`,
  harmonized_col_id = attributes(object)$`Rmonize::harmonized_col_id`,
  .debug = FALSE
)

Arguments

object

Data frame(s) or list of data frame(s) containing input dataset(s).

dataschema

A DataSchema object.

data_proc_elem

A Data Processing Elements object.

harmonized_col_dataset

A character string identifying the column to use for dataset names.

harmonized_col_id

A character string identifying the name of the column present in every dataset to use as a participant identifier.

.debug

Allow user to test the inputs before processing harmonization.

Details

A dossier is a named list containing one or more data frames, which are input datasets. The name of each data frame in the dossier will be used as the name of the associated harmonized dataset produced by harmo_process().

Value

A list of data frame(s) containing harmonized dataset(s). The DataSchema and Data Processing Elements are preserved as attributes of the harmonized dossier.

Examples


# Use Rmonize_examples to run examples.

library(dplyr)
library(stringr)
library(lubridate)

# Perform data processing
dossier <- Rmonize_examples[str_detect(names(Rmonize_examples),"input_dataset_study")]
names(dossier) <- str_remove(names(dossier), "input_")
dataschema <- Rmonize_examples$`DataSchema`
data_proc_elem <- Rmonize_examples$`Data_Processing_Elements_no_errors`

harmonized_dossier <- harmo_process(
  dossier,
  dataschema,
  data_proc_elem,
  harmonized_col_dataset = 'adm_study_id')
  
glimpse(harmonized_dossier$dataset_study1)

Generate an assessment report for a harmonized dossier

Description

Assesses the content and structure of a harmonized dossier and generates reports of the results. This function can be used to evaluate data structure, presence of specific fields, coherence across elements, and data dictionary formats.

Usage

harmonized_dossier_evaluate(harmonized_dossier)

Arguments

harmonized_dossier

A list containing the harmonized dataset(s).

Details

A harmonized dossier is a named list containing one or more data frames, which are harmonized datasets. A harmonized dossier is generally the product of applying processing to a dossier object. The name of each harmonized dataset (data frame) is taken from the reference input dataset. A harmonized dossier also contains the DataSchema and Data Processing Elements used in processing as attributes.

Value

A list of data frames containing assessment reports for each harmonized dataset.

Examples



# Use Rmonize_examples to run examples.
library(dplyr)

# Perform data processing
harmonized_dossier <- Rmonize_examples$`harmonized_dossier`

eval_harmo <- harmonized_dossier_evaluate(harmonized_dossier)

glimpse(eval_harmo)

Generate an assessment report and summary of a harmonized dossier

Description

Assesses and summarizes the content and structure of a harmonized dossier and generates reports of the results. This function can be used to evaluate data structure, presence of specific fields, coherence across elements, and data dictionary formats, and to summarize additional information about variable distributions and descriptive statistics.

Usage

harmonized_dossier_summarize(harmonized_dossier)

Arguments

harmonized_dossier

A list containing the harmonized dataset(s).

Details

A harmonized dossier is a named list containing one or more data frames, which are harmonized datasets. A harmonized dossier is generally the product of applying processing to a dossier object. The name of each harmonized dataset (data frame) is taken from the reference input dataset. A harmonized dossier also contains the DataSchema and Data Processing Elements used in processing as attributes.

The valueType is a declared property of a variable that is required in certain functions to determine handling of the variables. Specifically, valueType refers to the OBiBa data type of a variable. The valueType is specified in a data dictionary in a column 'valueType' and can be associated with variables as attributes. Acceptable valueTypes include 'text', 'integer', 'decimal', 'boolean', datetime', 'date'. The full list of OBiBa valueType possibilities and their correspondence with R data types are available using valueType_list. The valueType can be used to coerce the variable to the corresponding data type.

Value

A list of data frames containing overall assessment reports and summaries grouped by harmonized dataset.

Examples


# Use Rmonize_examples to run examples.
library(dplyr)

# Perform data processing
harmonized_dossier <- Rmonize_examples$`harmonized_dossier`
summary_harmo <- harmonized_dossier_summarize(harmonized_dossier)

glimpse(summary_harmo)

Generate a web-based visual report for a harmonized dossier

Description

Generates a visual report of a harmonized dossier in an HTML bookdown document, with summary figures and statistics for each harmonized variable. The report outputs can be grouped by a categorical variable.

Usage

harmonized_dossier_visualize(
  harmonized_dossier,
  bookdown_path,
  harmonized_dossier_summary = NULL
)

Arguments

harmonized_dossier

A list containing the harmonized dataset(s).

bookdown_path

A character string identifying the folder path where the bookdown report files will be saved.

harmonized_dossier_summary

A list which identifies an existing summary produced by harmonized_dossier_summarize() of the harmonized variables. Using this parameter can save time in generating the visual report.

Details

A harmonized dossier is a named list containing one or more data frames, which are harmonized datasets. A harmonized dossier is generally the product of applying processing to a dossier object. The name of each harmonized dataset (data frame) is taken from the reference input dataset. A harmonized dossier also contains the DataSchema and Data Processing Elements used in processing as attributes.

Value

A folder containing files for the bookdown document. To open the bookdown document in a browser, open 'docs/index.html', or use bookdown_open() with the folder path.

Examples



library(fs)

# Use Rmonize_examples to run examples.
# Perform data processing

harmonized_dossier <- Rmonize_examples$`harmonized_dossier`
harmonized_dossier_summary <- Rmonize_examples$`summary_report_harmonized_dossier`

# Create a folder where the visual report will be placed

if(dir_exists(tempdir())) dir_delete(tempdir())
bookdown_path <- tempdir()

# Generate the visual report
harmonized_dossier_visualize(
  harmonized_dossier = harmonized_dossier,
  bookdown_path = bookdown_path,
  harmonized_dossier_summary = harmonized_dossier_summary)
  
# To open the file in a browser, open 'bookdown_path/docs/index.html'.
# Or use bookdown_open(bookdown_path) function.

Test for a valid Data Processing Elements object

Description

Tests if the input is a valid Data Processing Elements object. This function mainly helps validate input within other functions of the package but could be used to check if an object is valid for use in a function.

Usage

is_data_proc_elem(object)

Arguments

object

A potential Data Processing Elements object to be evaluated.

Details

Value

A logical.

Examples

{

# Use Rmonize_examples to run examples.

is_data_proc_elem(Rmonize_examples$`Data_Processing_Elements_no_errors`)
is_data_proc_elem(Rmonize_examples$`DataSchema`)
is_data_proc_elem(iris)
}

Test for a valid DataSchema object

Description

Tests if the input is a valid DataSchema object. This function mainly helps validate input within other functions of the package but could be used to check if an object is valid for use in a function.

Usage

is_dataschema(object)

Arguments

object

A potential DataSchema object to be evaluated.

Details

Value

A logical.

Examples

{

# Use Rmonize_examples to run examples.

is_dataschema(Rmonize_examples$`DataSchema`)
is_dataschema(Rmonize_examples$`Data_Processing_Elements_no_errors`)
is_dataschema(iris)

}

Test for a valid DataSchema object with specific format restrictions

Description

Tests if an object is a valid DataSchema object with specific format restrictions for compatibility with other Maelstrom Research software. This function mainly helps validate input within other functions of the package but could be used to check if an object is valid for use in a function.

Usage

is_dataschema_mlstr(object)

Arguments

object

A potential DataSchema object to be evaluated.

Details

The object may be specifically formatted to be compatible with additional Maelstrom Research software, in particular Opal environments.

Value

A logical.

Examples

{

# use Rmonize_examples provided by the package

is_dataschema_mlstr(Rmonize_examples$`DataSchema`)
is_dataschema_mlstr(Rmonize_examples$`Data_Processing_Elements_no_errors`)
is_dataschema_mlstr(iris)

}

Generate a pooled harmonized dataset from a harmonized dossier

Description

Generates a pooled harmonized dataset from a harmonized dossier. The function has two arguments that can optionally be declared by the user (harmonized_col_dataset and harmonized_col_id). harmonized_col_dataset refers to the columns which contains name of each harmonized dataset. harmonized_col_id refers to the column in harmonized datasets which identifies unique combinations of observation/dataset. These two columns are added to ensure that there is always a unique entity identifier when datasets are pooled.

Usage

pooled_harmonized_dataset_create(
  harmonized_dossier,
  harmonized_col_dataset =
    attributes(harmonized_dossier)$`Rmonize::harmonized_col_dataset`,
  harmonized_col_id = attributes(harmonized_dossier)$`Rmonize::harmonized_col_id`,
  dataschema = attributes(harmonized_dossier)$`Rmonize::DataSchema`,
  data_proc_elem = attributes(harmonized_dossier)$`Rmonize::Data Processing Elements`
)

Arguments

harmonized_dossier

A list containing the harmonized dataset(s).

harmonized_col_dataset

A character string identifying the column to use for dataset names.

harmonized_col_id

A character string identifying the name of the column present in every dataset to use as a participant identifier.

dataschema

A DataSchema object.

data_proc_elem

A Data Processing Elements object.

Details

Value

A data frame containing the pooled harmonized dataset.

Examples

{

# Use Rmonize_examples to run examples.
 
library(dplyr)

# Perform data processing
harmonized_dossier <- Rmonize_examples["harmonized_dossier"][[1]]

# create the pooled harmonized dataset from the harmonized dossier
pooled_harmonized_dataset <- pooled_harmonized_dataset_create(harmonized_dossier)
  
glimpse(pooled_harmonized_dataset)
  
}

Objects exported from other packages

Description

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

madshapR: as_category, as_data_dict_mlstr, as_taxonomy, as_valueType, col_id, data_dict_filter, data_dict_match_dataset, data_dict_trim_labels, data_dict_update, data_extract, dataset_zap_data_dict, first_label_get, has_categories, is_category, is_data_dict, is_data_dict_mlstr, is_dataset, is_dossier, is_taxonomy, valueType_adjust, valueType_self_adjust

Print a summary of data processing in the console

Description

Reads a harmonized dossier, product of harmo_process(), to list processes, any errors, and an overview of each harmonization rule. The output printed in the console can help in correcting any errors that occurred during data processing.

Usage

show_harmo_error(harmonized_dossier, show_warnings = TRUE)

Arguments

harmonized_dossier

A list containing the harmonized dataset(s).

show_warnings

Whether the function should print warnings or not. TRUE by default.

Details

Value

Nothing to be returned. The function prints messages in the console, showing any errors in data processing.

Examples

{
# Use Rmonize_examples to run examples.
library(dplyr)

# Perform data processing
harmonized_dossier <- Rmonize_examples$`harmonized_dossier`

# Show error(s) on the console
show_harmo_error(harmonized_dossier)
}

Rmonize: Tools for Data Harmonization

Description

Author(s)

See Also

Example objects to provide an illustrative use case

Description

Usage

Format

list

Examples

Call to online documentation to download templates

Description

Usage

Value

Examples

Call to package website

Description

Usage

Value

Examples

Objects exported from other packages

Description

Validate and coerce as a Data Processing Elements object

Description

Usage

Arguments

Details

Value

Examples

Validate and coerce as a DataSchema object

Description

Usage

Arguments

Details

Value

Examples

Validate and coerce as a DataSchema object with specific format restrictions

Description

Usage

Arguments

Details

Value

Examples

Objects exported from other packages

Description

Objects exported from other packages

Description

Validate and coerce as a harmonized dossier object

Description

Usage

Arguments

Details

Value

Examples

Objects exported from other packages

Description

Objects exported from other packages

Description

Objects exported from other packages

Description

Objects exported from other packages

Description

Generate an assessment report for Data Processing Elements

Description

Usage

Arguments

Details

Value

Examples

Generate an assessment report for a DataSchema

Description

Usage

Arguments

Details

Value

Examples

Generate a DataSchema based on Data Processing Elements

Description

Usage

Arguments

`list`