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Description

Support Retrospective Harmonization of Data.

Functions to support rigorous retrospective data harmonization processing, evaluation, and documentation across datasets from different studies based on Maelstrom Research guidelines. The package includes the core functions to evaluate and format the main inputs that define the harmonization process, apply specified processing rules to generate harmonized data, diagnose processing errors, and summarize and evaluate harmonized outputs. The main inputs that define the processing are a DataSchema (list and definitions of harmonized variables to be generated) and Data Processing Elements (processing rules to be applied to generate harmonized variables from study-specific variables). The main outputs of processing are harmonized datasets, associated metadata, and tabular and visual summary reports. As described in Maelstrom Research guidelines for rigorous retrospective data harmonization (Fortier I and al. (2017) <doi:10.1093/ije/dyw075>).

Rmonize

R-CMD-check

Overview

Harmonizing data (processing data items from different datasets under a common format) is essential to support research but can be methodologically and technically challenging. Rmonize is an R package developed by Maelstrom Research to address some of the key challenges faced and promote a streamlined, reusable, and well documented harmonization pipeline. The current documentation provides a starting point to use the package.

Pipeline


Data processing in Rmonize depends on three external user-provided elements: the input datasets (datasets collected by individual studies or data collection centres), DataSchema (list of core variables to generate across input datasets), and Data Processing Elements (elements and algorithms needed to process variables from input datasets into DataSchema formats). The DataSchema and Data Processing Elements are prepared in Excel spreadsheets and imported into R, and they can be easily modified and shared outside of R.


The package includes integrated functions to support organized data processing and generate well documented outputs. These functions help to prepare and validate inputs, process input datasets into harmonized datasets, identify and troubleshoot errors in processing elements, and produce documentation to help users evaluate harmonized data content and quality. The main outputs provided by Rmonize are the harmonized datasets, their associated data dictionaries, and reports with descriptive statistics, provided in summary tables or figures.

Rmonize also uses two underlying packages, madshapR and fabR, which include many functions to work with data and metadata. The specific functions required by Rmonize are automatically loaded and accessible to the user without separately loading madshapR and fabR.

Installation

# To install Rmonize:
install.packages('Rmonize')

library(Rmonize)
# If you need help with the package, please use:
Rmonize_website()

# Downloadable templates are available here
Rmonize_templates()

# Demo files are available here, along with an online demonstration process 
Rmonize_DEMO

Getting started

For more information, you can go to:

Documentation on Rmonize functions and help pages.

Descriptions of key terms and downloadable templates.

Explanation of how to prepare the Data Processing Elements.

Example scripts with demo files.

Metadata

Version

1.1.0

License

Unknown

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