Package: mwana 0.2.0

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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mwana.pdf |mwana.html✨
mwana/json (API)
NEWS

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

Peer review:

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

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 of a community-based sentinel site from an anonymized location
  • mfaz.01 - A sample MUAC screening data from an anonymized setting
  • mfaz.02 - A sample SMART survey data with MUAC
  • wfhz.01 - A sample SMART survey data with WFHZ standard deviation rated as problematic

On CRAN:

4.65 score 2 stars 6 scripts 22 exports 65 dependencies

Last updated 2 days agofrom:e447f17aa0. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 19 2024
R-4.5-winOKNov 19 2024
R-4.5-linuxOKNov 19 2024
R-4.4-winOKNov 19 2024
R-4.4-macOKNov 19 2024
R-4.3-winOKNov 19 2024
R-4.3-macOKNov 19 2024

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

Checking if IPC Acute Malnutrition sample size requirements were met

Rendered fromipc_amn_check.qmdusingquarto::htmlon Nov 19 2024.

Last update: 2024-11-17
Started: 2024-11-04

Estimating the prevalence of wasting

Rendered fromprevalence.qmdusingquarto::htmlon Nov 19 2024.

Last update: 2024-11-17
Started: 2024-10-22

Running plausibility checks

Rendered fromplausibility.qmdusingquarto::htmlon Nov 19 2024.

Last update: 2024-11-17
Started: 2024-10-24

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 of a community-based sentinel site from an anonymized locationanthro.04
Define wastingdefine_wasting
Identify, flag outliers and remove themflag_outliers remove_flags
Calculate child's age in monthsget_age_months
A sample MUAC screening data from an anonymized settingmfaz.01
A sample SMART survey data with MUACmfaz.02
Check whether IPC Acute Malnutrition (IPC AMN) sample size requirements were 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 z-scores of weight-for-height (WFHZ)mw_estimate_prevalence_wfhz
Clean and format the output table returned from the MFAZ plausibility check for improved clarity and readabilitymw_neat_output_mfaz
Clean and format the output table returned from the MUAC plausibility check for improved clarity and readability.mw_neat_output_muac
Clean and format the output table returned from the WFHZ plausibility check for improved clarity and readabilitymw_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 WFHZ standard deviation rated as problematicwfhz.01