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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:	no
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 Fabreguijoseph.fabre@gmail.com (ORCID)

Authors:

• Maelstrom Researchinfo@maelstrom-research.org [funder, copyright holder]

See Also

Useful links:

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

• Report bugs athttps://github.com/maelstrom-research/Rmonize/issues

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 objectsfor 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 theRmonize process, vignettes and user guides, a reference list of functionsand 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 linksbelow 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 withthe appropriateRmonize::class attribute. This function mainly helpsvalidate inputs within other functions of the package but could be usedseparately to ensure that an object has an appropriate structure.

Usage

as_data_proc_elem(object)

Arguments

Details

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

Value

A data frame withRmonize::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 appropriateRmonize::class attribute. This function mainly helps validate inputs withinother functions of the package but could be used separately to ensure that anobject has an appropriate structure.

Usage

as_dataschema(object, as_dataschema_mlstr = FALSE)

Arguments

Details

A DataSchema is the list of core variables to generate across datasets andrelated metadata. A DataSchema object is a list of data frames with elementsnamed 'Variables' (required) and 'Categories' (if any). The 'Variables'element must contain at least thename column, and the 'Categories'element must contain at least thevariable andname columns to be usablein any function. In 'Variables' thename column must also have uniqueentries, and in 'Categories' the combination ofvariable andname columnsmust also be unique.

The object may be specifically formatted to be compatible with additionalMaelstrom Research software,in particularOpal environments.

Value

A list of data frame(s) named 'Variables' and (if any) 'Categories',withRmonize::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 restrictionsfor compatibility with other Maelstrom Research software and returns it withthe appropriateRmonize::class attribute. This function mainly helps validateinputs within other functions of the package but could be used separately toensure that an object has an appropriate structure.

Usage

as_dataschema_mlstr(object)

Arguments

Details

A DataSchema is the list of core variables to generate across datasets andrelated metadata. A DataSchema object is a list of data frames with elementsnamed 'Variables' (required) and 'Categories' (if any). The 'Variables'element must contain at least thename column, and the 'Categories'element must contain at least thevariable andname columns to be usablein any function. In 'Variables' thename column must also have uniqueentries, and in 'Categories' the combination ofvariable andname columnsmust also be unique.

The object may be specifically formatted to be compatible with additionalMaelstrom Research software,in particularOpal environments.

Value

A list of data frame(s) named 'Variables' and (if any) 'Categories', withRmonize::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 linksbelow to see their documentation.

madshapR

as_dataset

Objects exported from other packages

Description

These objects are imported from other packages. Follow the linksbelow 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 theappropriateRmonize::class attribute. This function mainly helps validateinputs within other functions of the package but could be used separately toensure that an object has an appropriate structure. The functionhas two arguments that can optionally be declared by the user(unique_col_dataset andunique_col_id).unique_col_dataset refers tothe columns which contains name of each harmonized dataset.unique_col_idrefers to the column in harmonized datasets which identifies uniquecombinations of observation/dataset. These two columns are added to ensurethat 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

Details

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

A DataSchema is the list of core variables to generate across datasets andrelated metadata. A DataSchema object is a list of data frames with elementsnamed 'Variables' (required) and 'Categories' (if any). The 'Variables'element must contain at least thename column, and the 'Categories'element must contain at least thevariable andname columns to be usablein any function. In 'Variables' thename column must also have uniqueentries, and in 'Categories' the combination ofvariable andname columnsmust also be unique.

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

Value

A list of data frame(s) containing harmonized dataset(s).The DataSchema and Data Processing Elements are preserved as attributes ofthe 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 linksbelow to see their documentation.

madshapR

bookdown_open

Objects exported from other packages

Description

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

madshapR

data_dict_apply

Objects exported from other packages

Description

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

madshapR

data_dict_evaluate

Objects exported from other packages

Description

These objects are imported from other packages. Follow the linksbelow 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 andgenerates reports of the results. This function can be used to evaluate datastructure, presence of specific fields, coherence across elements, and datadictionary formats.

Usage

data_proc_elem_evaluate(data_proc_elem, taxonomy = NULL)

Arguments

Details

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

A taxonomy is a classification schema that can be defined for variableattributes. A taxonomy is usually extracted from anOpal environment, and ataxonomy object is a data frame that must contain at least the columnstaxonomy,vocabulary, andterms. Additional details about Opaltaxonomies areavailable 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 generatesreports of the results. This function can be used to evaluate data structure,presence of specific fields, coherence across elements, and data dictionaryformats.

Usage

dataschema_evaluate(dataschema, taxonomy = NULL)

Arguments

Details

A DataSchema is the list of core variables to generate across datasets andrelated metadata. A DataSchema object is a list of data frames with elementsnamed 'Variables' (required) and 'Categories' (if any). The 'Variables'element must contain at least thename column, and the 'Categories'element must contain at least thevariable andname columns to be usablein any function. In 'Variables' thename column must also have uniqueentries, and in 'Categories' the combination ofvariable andname columnsmust also be unique.

A taxonomy is a classification schema that can be defined for variableattributes. A taxonomy is usually extracted from anOpal environment, and ataxonomy object is a data frame that must contain at least the columnstaxonomy,vocabulary, andterms. Additional details about Opaltaxonomies areavailable online.

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

Details

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

A DataSchema is the list of core variables to generate across datasets andrelated metadata. A DataSchema object is a list of data frames with elementsnamed 'Variables' (required) and 'Categories' (if any). The 'Variables'element must contain at least thename column, and the 'Categories'element must contain at least thevariable andname columns to be usablein any function. In 'Variables' thename column must also have uniqueentries, and in 'Categories' the combination ofvariable andname columnsmust also be unique.The object may be specifically formatted to be compatible with additionalMaelstrom Research software,in particularOpal environments.

Value

A list of data frame(s) named 'Variables' and (if any) 'Categories', withRmonize::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 linksbelow to see their documentation.

madshapR

dataset_evaluate

Objects exported from other packages

Description

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

madshapR

dataset_summarize

Objects exported from other packages

Description

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

madshapR

dataset_visualize

Objects exported from other packages

Description

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

madshapR

dossier_create

Objects exported from other packages

Description

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

madshapR

dossier_evaluate

Objects exported from other packages

Description

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

madshapR

dossier_summarize

Generate harmonized dataset(s) and associated metadata

Description

Reads a DataSchema and Data Processing Elements to generate aharmonized dossier from input dataset(s) in a dossier and associatedmetadata. The functionhas one argument that can optionally be declared by the user(unique_col_dataset). It refers to the columns which contains name ofeach harmonized dataset. These two columns are added to ensure that thereis 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

Details

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

A DataSchema is the list of core variables to generate across datasets andrelated metadata. A DataSchema object is a list of data frames with elementsnamed 'Variables' (required) and 'Categories' (if any). The 'Variables'element must contain at least thename column, and the 'Categories'element must contain at least thevariable andname columns to be usablein any function. In 'Variables' thename column must also have uniqueentries, and in 'Categories' the combination ofvariable andname columnsmust also be unique.

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

Value

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

Examples

# Use Rmonize_examples to run examples.library(dplyr)library(stringr)library(lubridate)# Perform data processingdossier <- 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 generatesreports of the results. This function can be used to evaluate data structure,presence of specific fields, coherence across elements, and data dictionaryformats.

Usage

harmonized_dossier_evaluate(harmonized_dossier)

Arguments

Details

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

A DataSchema is the list of core variables to generate across datasets andrelated metadata. A DataSchema object is a list of data frames with elementsnamed 'Variables' (required) and 'Categories' (if any). The 'Variables'element must contain at least thename column, and the 'Categories'element must contain at least thevariable andname columns to be usablein any function. In 'Variables' thename column must also have uniqueentries, and in 'Categories' the combination ofvariable andname columnsmust also be unique.

A taxonomy is a classification schema that can be defined for variableattributes. A taxonomy is usually extracted from anOpal environment, and ataxonomy object is a data frame that must contain at least the columnstaxonomy,vocabulary, andterms. Additional details about Opaltaxonomies areavailable online.The object may be specifically formatted to be compatible with additionalMaelstrom Research software,in particularOpal environments.

Value

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

Examples

# Use Rmonize_examples to run examples.library(dplyr)# Perform data processingharmonized_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 andgenerates reports of the results. This function can be used to evaluate datastructure, presence of specific fields, coherence across elements, anddata dictionary formats, and to summarize additional information aboutvariable distributions and descriptive statistics.

Usage

harmonized_dossier_summarize(harmonized_dossier)

Arguments

Details

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

A DataSchema is the list of core variables to generate across datasets andrelated metadata. A DataSchema object is a list of data frames with elementsnamed 'Variables' (required) and 'Categories' (if any). The 'Variables'element must contain at least thename column, and the 'Categories'element must contain at least thevariable andname columns to be usablein any function. In 'Variables' thename column must also have uniqueentries, and in 'Categories' the combination ofvariable andname columnsmust also be unique.

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

A taxonomy is a classification schema that can be defined for variableattributes. A taxonomy is usually extracted from anOpal environment, and ataxonomy object is a data frame that must contain at least the columnstaxonomy,vocabulary, andterms. Additional details about Opaltaxonomies areavailable online.

The valueType is a declared property of a variable that is required incertain functions to determine handling of the variables. Specifically,valueType refers to theOBiBa data type of a variable.The valueType is specified in a data dictionary in a column 'valueType' andcan be associated with variables as attributes. Acceptable valueTypesinclude 'text', 'integer', 'decimal', 'boolean', datetime', 'date'. The fulllist of OBiBa valueType possibilities and their correspondence with R datatypes are available usingvalueType_list. The valueType can be used tocoerce the variable to the corresponding data type.

Value

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

Examples

# Use Rmonize_examples to run examples.library(dplyr)# Perform data processingharmonized_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 bookdowndocument, 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

Details

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

Value

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

See Also

dataset_visualize()bookdown_open()

Examples

library(fs)# Use Rmonize_examples to run examples.# Perform data processingharmonized_dossier <- Rmonize_examples$`harmonized_dossier`harmonized_dossier_summary <- Rmonize_examples$`summary_report_harmonized_dossier`# Create a folder where the visual report will be placedif(dir_exists(tempdir())) dir_delete(tempdir())bookdown_path <- tempdir()# Generate the visual reportharmonized_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 functionmainly helps validate input within other functions of the package but couldbe used to check if an object is valid for use in a function.

Usage

is_data_proc_elem(object)

Arguments

Details

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

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 helpsvalidate input within other functions of the package but could be used tocheck if an object is valid for use in a function.

Usage

is_dataschema(object)

Arguments

Details

A DataSchema is the list of core variables to generate across datasets andrelated metadata. A DataSchema object is a list of data frames with elementsnamed 'Variables' (required) and 'Categories' (if any). The 'Variables'element must contain at least thename column, and the 'Categories'element must contain at least thevariable andname columns to be usablein any function. In 'Variables' thename column must also have uniqueentries, and in 'Categories' the combination ofvariable andname columnsmust also be unique.

Value

A logical.

See Also

dataschema_evaluate().

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 formatrestrictions for compatibility with other Maelstrom Research software. Thisfunction mainly helps validate input within other functions of the packagebut could be used to check if an object is valid for use in a function.

Usage

is_dataschema_mlstr(object)

Arguments

Details

A DataSchema is the list of core variables to generate across datasets andrelated metadata. A DataSchema object is a list of data frames with elementsnamed 'Variables' (required) and 'Categories' (if any). The 'Variables'element must contain at least thename column, and the 'Categories'element must contain at least thevariable andname columns to be usablein any function. In 'Variables' thename column must also have uniqueentries, and in 'Categories' the combination ofvariable andname columnsmust also be unique.

The object may be specifically formatted to be compatible with additionalMaelstrom Research software,in particularOpal environments.

Value

A logical.

See Also

dataschema_evaluate().

Examples

{# use Rmonize_examples provided by the packageis_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 functionhas two arguments that can optionally be declared by the user(harmonized_col_dataset andharmonized_col_id).harmonized_col_datasetrefers to the columns which contains name of each harmonized dataset.harmonized_col_id refers to the column in harmonized datasets whichidentifies unique combinations of observation/dataset. These two columns areadded to ensure that there is always a unique entity identifier when datasetsare 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

Details

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

A DataSchema is the list of core variables to generate across datasets andrelated metadata. A DataSchema object is a list of data frames with elementsnamed 'Variables' (required) and 'Categories' (if any). The 'Variables'element must contain at least thename column, and the 'Categories'element must contain at least thevariable andname columns to be usablein any function. In 'Variables' thename column must also have uniqueentries, and in 'Categories' the combination ofvariable andname columnsmust also be unique.

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

Value

A data frame containing the pooled harmonized dataset.

Examples

{# Use Rmonize_examples to run examples. library(dplyr)# Perform data processingharmonized_dossier <- Rmonize_examples["harmonized_dossier"][[1]]# create the pooled harmonized dataset from the harmonized dossierpooled_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 linksbelow 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 ofharmo_process(), to list processes,any errors, and an overview of each harmonization rule. The output printedin the console can help in correcting any errors that occurred duringdata processing.

Usage

show_harmo_error(harmonized_dossier, show_warnings = TRUE)

Arguments

Details

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

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 processingharmonized_dossier <- Rmonize_examples$`harmonized_dossier`# Show error(s) on the consoleshow_harmo_error(harmonized_dossier)}