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Description

National Information Platforms for Nutrition Anthropometric Data Toolkit.

An implementation of the National Information Platforms for Nutrition or NiPN's analytic methods for assessing quality of anthropometric datasets that include measurements of weight, height or length, middle upper arm circumference, sex and age. The focus is on anthropometric status but many of the presented methods could be applied to other variables.

nipnTK: National Information Platforms for Nutrition (NiPN) Data Quality Toolkit

Project Status: Active – The project has reached a stable, usablestate and is being activelydeveloped. Lifecycle:stable CRAN cranchecks CRAN CRAN CRAN R-CMD-check test-coverage Codecov testcoverage CodeFactor DOI

National Information Platforms for Nutrition (NiPN) is an initiative of the European Commission to provide support to countries to strengthen their information systems for nutrition and to improve the analysis of data so as to better inform the strategic decisions they are faced with to prevent malnutrition and its consequences.

As part of this mandate, NiPN has commissioned work on the development of a toolkit to assess the quality of various nutrition-specific and nutrition-related data. This is a companion R package to the toolkit of practical analytical methods that can be applied to variables in datasets to assess their quality.

The focus of the toolkit is on data required to assess anthropometric status such as measurements of weight, height or length, MUAC, sex and age. The focus is on anthropometric status but many of presented methods could be applied to other types of data. NiPN may commission additional toolkits to examine other variables or other types of variables.

Installation

You can install nipnTK from CRAN:

install.packages("nipnTK")

You can install the development version of nipnTK from GitHub with:

if(!require(remotes)) install.packages("remotes")
remotes::install_github("nutriverse/nipnTK")

Usage

Data quality is assessed by:

  1. Range checks and value checks to identify univariate outliers - guide

  2. Scatterplots and statistical methods to identify bivariate outliers - guide

  3. Use of flags to identify outliers in anthropometric indices - guide

  4. Examining the distribution and the statistics of the distribution of measurements and anthropometric indices - guide

  5. Assessing the extent of digit preference in recorded measurements - guide

  6. Assessing the extent of age heaping in recorded ages - guide

  7. Examining the sex ratio - guide

  8. Examining age distributions and age by sex distributions - guide

These activities and a proposed order in which they should be performed are shown below:

Citation

If you find the nipnTK package useful, please cite using the suggested citation provided by a call to the citation function as follows:

citation("nipnTK")
#> To cite nipnTK in publications use:
#> 
#>   Mark Myatt, Ernest Guevarra (2024). _nipnTK: National Information
#>   Platforms for Nutrition (NiPN) Data Quality Toolkit_.
#>   doi:10.5281/zenodo.4297897 <https://doi.org/10.5281/zenodo.4297897>,
#>   R package version 0.2.0, <https://nutriverse.io/nipnTK/>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {nipnTK: National Information Platforms for Nutrition (NiPN) Data Quality Toolkit},
#>     author = {{Mark Myatt} and {Ernest Guevarra}},
#>     year = {2024},
#>     note = {R package version 0.2.0},
#>     url = {https://nutriverse.io/nipnTK/},
#>     doi = {10.5281/zenodo.4297897},
#>   }

Community guidelines

Feedback, bug reports and feature requests are welcome; file issues or seek support here. If you would like to contribute to the package, please see our contributing guidelines.

This project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

Metadata

Version

0.2.0

License

Unknown

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