Package: mwana 0.2.1.9000

Tomás Zaba

mwana: An Efficient Workflow for Plausibility Checks and Prevalence Analysis of Wasting in R

A simple and streamlined workflow for plausibility checks and prevalence analysis of wasting based on the Standardized Monitoring and Assessment of Relief and Transition (SMART) Methodology <https://smartmethodology.org/>, with application in R.

Authors:Tomás Zaba [aut, cre, cph], Ernest Guevarra [aut, cph]

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NEWS

# Install 'mwana' in R:
install.packages('mwana', repos = c('https://nutriverse.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/nutriverse/mwana/issues

Pkgdown site:https://nutriverse.io

Datasets:
  • anthro.01 - A sample data of district level SMART surveys with location anonymised
  • anthro.02 - A sample of an already wrangled survey data
  • anthro.03 - A sample data of district level SMART surveys conducted in Mozambique
  • anthro.04 - A sample data from a community-based sentinel site in an anonymized location
  • mfaz.01 - A sample mid-upper arm circumference (MUAC) screening data
  • mfaz.02 - A sample SMART survey data with mid-upper arm circumference measurements
  • wfhz.01 - A sample SMART survey data with weight-for-height z-score standard deviation rated as problematic

On CRAN:

Conda:

acute-malnutritionanthropometrymuacnutritionsmartsurveywasting

4.23 score 2 stars 6 scripts 22 exports 65 dependencies

Last updated 1 months agofrom:c737d7dd3f. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 07 2025
R-4.5-winOKMar 07 2025
R-4.5-macOKMar 07 2025
R-4.5-linuxOKMar 07 2025
R-4.4-winOKMar 07 2025
R-4.4-macOKMar 07 2025
R-4.4-linuxOKMar 07 2025
R-4.3-winOKMar 07 2025
R-4.3-macOKMar 07 2025

Exports:define_wastingflag_outliersget_age_monthsmw_check_ipcamn_ssreqmw_estimate_prevalence_combinedmw_estimate_prevalence_mfazmw_estimate_prevalence_muacmw_estimate_prevalence_screeningmw_estimate_prevalence_wfhzmw_estimate_smart_age_wtmw_neat_output_mfazmw_neat_output_muacmw_neat_output_wfhzmw_plausibility_check_mfazmw_plausibility_check_muacmw_plausibility_check_wfhzmw_stattest_ageratiomw_wrangle_agemw_wrangle_muacmw_wrangle_wfhzrecode_muacremove_flags

Dependencies:base64encbslibcachemclicolorspacecommonmarkcpp11crayonDBIdigestdplyrfansifarverfastmapfontawesomefsgenericsgluehtmltoolshttpuvjquerylibjsonlitelabelinglaterlatticelifecyclelubridatemagrittrMatrixmemoisemimeminqamitoolsmunsellnipnTKnumDerivpillarpkgconfigpromisespurrrR6rappdirsRColorBrewerRcppRcppArmadillorlangsassscalesshinysourcetoolssrvyrstringistringrsurveysurvivaltibbletidyrtidyselecttimechangeutf8vctrsviridisLitewithrxtablezscorer

Estimating the prevalence of wasting

Rendered fromprevalence.qmdusingquarto::htmlon Mar 07 2025.

Last update: 2024-12-07
Started: 2024-10-22

Readme and manuals

Help Manual

Help pageTopics
A sample data of district level SMART surveys with location anonymisedanthro.01
A sample of an already wrangled survey dataanthro.02
A sample data of district level SMART surveys conducted in Mozambiqueanthro.03
A sample data from a community-based sentinel site in an anonymized locationanthro.04
Define wastingdefine_wasting
Identify, flag, and remove outliersflag_outliers remove_flags
Calculate child's age in monthsget_age_months
A sample mid-upper arm circumference (MUAC) screening datamfaz.01
A sample SMART survey data with mid-upper arm circumference measurementsmfaz.02
Check whether sample size requirements for IPC Acute Malnutrition (IPC AMN) analysis are metmw_check_ipcamn_ssreq
Estimate the prevalence of combined wastingmw_estimate_prevalence_combined
Estimate the prevalence of wasting based on z-scores of muac-for-age (MFAZ)mw_estimate_prevalence_mfaz
Estimate the prevalence of wasting based on MUAC for survey datamw_estimate_prevalence_muac mw_estimate_smart_age_wt
Estimate the prevalence of wasting based on MUAC for non-survey datamw_estimate_prevalence_screening
Estimate the prevalence of wasting based on weight-for-height z-scores (WFHZ)mw_estimate_prevalence_wfhz
Clean and format the output tibble returned from the MUAC-for-age z-score plausibility checkmw_neat_output_mfaz
Clean and format the output tibble returned from the MUAC plausibility checkmw_neat_output_muac
Clean and format the output tibble returned from the WFHZ plausibility checkmw_neat_output_wfhz
Check the plausibility and acceptability of MUAC-for-age z-score (MFAZ) datamw_plausibility_check_mfaz
Check the plausibility and acceptability of raw MUAC datamw_plausibility_check_muac
Check the plausibility and acceptability of weight-for-height z-score (WFHZ) datamw_plausibility_check_wfhz
Test for statistical difference between the proportion of children aged 24 to 59 months old over those aged 6 to 23 months oldmw_stattest_ageratio
Wrangle child's agemw_wrangle_age
Wrangle MUAC datamw_wrangle_muac
Wrangle weight-for-height datamw_wrangle_wfhz
Convert MUAC values to either centimeters or millimetersrecode_muac
A sample SMART survey data with weight-for-height z-score standard deviation rated as problematicwfhz.01