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

Project Status: Active – The project has reached a stable, usable state and is being actively developed. lifecycle CRAN CRAN CRAN R build status AppVeyor Build Status R build status codecov

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.


Extensive use is made of the R language and environment for statistical computing. This is a free and powerful data analysis system. R provides a very extensive language for working with data. This companion package has been written using only a small subset of the R language. Many of the data quality activities described in the toolkit are supported by R functions included in this package that have been written specifically for this purpose. These simplify the assessment of the quality of data related to anthropometry and anthropometric indices.


You can install nipnTK from CRAN:


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

if(!require(remotes)) install.packages("remotes")


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:

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.