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---
format: gfm
---
<!-- README.md is generated from README.qmd. Please edit that file -->
```{r}
#| label: load_library
#| include: false
library(mwana)
```
# mwana: An efficient workflow for plausibility checks and prevalence analysis of wasting in R <img src="man/figures/logo.png" align="right" width="200px" />
<!-- badges: start -->
[](https://www.repostatus.org/#active)
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
[](https://github.com/mphimo/mwana/actions/workflows/R-CMD-check.yaml)
[](https://github.com/mphimo/mwana/actions/workflows/test-coverage.yaml)
[](https://codecov.io/gh/mphimo/mwana)
<!-- badges: end -->
Child anthropometric assessments are central to child nutrition and food-security surveillance worldwide. Ensuring the quality of these data is essential for accurate estimates of child undernutrition prevalence. Timely reporting is also critical for situation analysis and for mounting effective responses.
The `mwana` package streamlines data-quality checks and acute-undernutrition prevalence estimation from anthropometric data for children aged 6–59 months. It builds on methods and guidance from the [SMART initiative](https://smartmethodology.org) and provides convenient wrappers around functions in the [`nipnTK`](https://nutriverse.io/nipnTK) package.
The term ***mwana*** means child in *Elómwè*, a local language spoken in the central-northern regions of Mozambique where the author hails from. It also has a similar meaning across other Bantu languages, such as *Swahili*, spoken in many parts of Africa.
## Motivation
The standard child-anthropometry appraisal workflow is complex and time-consuming, relying on multiple tools—such as SPSS, Microsoft Excel, and SMART ENA software—for various steps of the process. Each dataset requires the repetition of these steps—often under tight deadlines, which makes the manual and repetitive workflow highly error-prone.
`mwana` provides functions that simplify this cumbersome workflow, enabling it to be programmatically designed, particularly when handling multi-area datasets.
## Installation
`mwana` is not yet on CRAN but can be installed from GitHub:
```{r}
#| label: installation
#| eval: false
# First install remotes package with: install.package("remotes")
# Then install mwana package from GitHub with:
remotes::install_github(repo = "mphimo/mwana", dependencies = TRUE)
```
## What does `mwana` do?
<img src="man/figures/workflow.png" width="360px" height="900px" align="left" />Currently, `mwana` has the following functionalities that support the creation of a programmatic workflow illustrated in the figure to the left.
### 1. Data plausibility checks of acute undernutrition anthropometric data of children 6-59 months old
`mwana` provides functions to perform data plausibility checks on weight-for-height z-score (WFHZ) data. These are based on the SMART plausibility checkers, data quality scoring, and classification criteria implemented in the ENA for SMART software. Moreover, it provides functions to perform data plausibility checks on Mid-Upper Arm Circumference (MUAC) data. These are based on recent research and recommendations concerning the MUAC-for-age z-score (MFAZ) and its utility for assessing the plausibility of MUAC data. To learn more, see [Plausibility check guide](https://mphimo.github.io/mwana/articles/plausibility.html).
### 2. Prevalence estimation of acute undernutrition
`mwana` provides prevalence estimators that follow SMART guidelines on the estimation approach to apply, based on an assessment of data quality. These functions accept datasets containing multiple survey domains and produce summary tables with prevalence estimates for each domain.
### 3. IPC sample size checker
`mwana` provides a function to check whether each domain in an anthropometric dataset meets IPC minimum sample-size requirements. The check accounts for the data-collection mode (survey, screening exercise, or sentinel-site surveillance). Read [IPC check guide](https://mphimo.github.io/mwana/articles/ipc-amn-check.html).
### 4. Reporting of data plausibility checks and prevalence estimation summary outputs
`mwana` includes helper functions that process summary outputs into presentation- or report-ready tables.
::: {.callout-tip}
If you are researching anthropometric data for children aged 6–59 months (focusing on acute undernutrition), `mwana` includes functions to wrangle weight, height, age, WFHZ, MUAC, and MFAZ prior to analysis.
:::
### Shiny App
This package has a lightweight, field-ready and convenient web-based application (`mwanaApp`) that enables users to upload their data and benefit from the `mwana` utilities needless to be well versed in R. Learn more about `mwanaApp` and how to install and use [here](https://github.com/mphimo/mwanaApp.git).
## Citation
If you use `mwana` package in your work, please cite using the suggested citation provided by a call to `citation()` function as follows:
```{r}
#| label: citation
#| echo: true
citation("mwana")
```
## Community guidelines
Feedback, bug reports and feature requests are welcome; file issues or seek support [here](https://github.com/mphimo/mwana/issues). If you would like to contribute to the package, please see our [contributing guidelines](https://mphimo.github.io/mwana/CONTRIBUTING.html).
This project is released with a [Contributor Code of Conduct](https://mphimo.github.io/mwana/CODE-OF-CONDUCT.html). By participating in this project you agree to abide by its terms.