Package: mMARCH.AC 2.9.4.0

Wei Guo

mMARCH.AC: Processing of Accelerometry Data with 'GGIR' in mMARCH

Mobile Motor Activity Research Consortium for Health (mMARCH) is a collaborative network of studies of clinical and community samples that employ common clinical, biological, and digital mobile measures across involved studies. One of the main scientific goals of mMARCH sites is developing a better understanding of the inter-relationships between accelerometry-measured physical activity (PA), sleep (SL), and circadian rhythmicity (CR) and mental and physical health in children, adolescents, and adults. Currently, there is no consensus on a standard procedure for a data processing pipeline of raw accelerometry data, and few open-source tools to facilitate their development. The R package 'GGIR' is the most prominent open-source software package that offers great functionality and tremendous user flexibility to process raw accelerometry data. However, even with 'GGIR', processing done in a harmonized and reproducible fashion requires a non-trivial amount of expertise combined with a careful implementation. In addition, novel accelerometry-derived features of PA/SL/CR capturing multiscale, time-series, functional, distributional and other complimentary aspects of accelerometry data being constantly proposed and become available via non-GGIR R implementations. To address these issues, mMARCH developed a streamlined harmonized and reproducible pipeline for loading and cleaning raw accelerometry data, extracting features available through 'GGIR' as well as through non-GGIR R packages, implementing several data and feature quality checks, merging all features of PA/SL/CR together, and performing multiple analyses including Joint Individual Variation Explained (JIVE), an unsupervised machine learning dimension reduction technique that identifies latent factors capturing joint across and individual to each of three domains of PA/SL/CR. In detail, the pipeline generates all necessary R/Rmd/shell files for data processing after running 'GGIR' for accelerometer data. In module 1, all csv files in the 'GGIR' output directory were read, transformed and then merged. In module 2, the 'GGIR' output files were checked and summarized in one excel sheet. In module 3, the merged data was cleaned according to the number of valid hours on each night and the number of valid days for each subject. In module 4, the cleaned activity data was imputed by the average Euclidean norm minus one (ENMO) over all the valid days for each subject. Finally, a comprehensive report of data processing was created using Rmarkdown, and the report includes few exploratory plots and multiple commonly used features extracted from minute level actigraphy data. Reference: Guo W, Leroux A, Shou S, Cui L, Kang S, Strippoli MP, Preisig M, Zipunnikov V, Merikangas K (2022) Processing of accelerometry data with GGIR in Motor Activity Research Consortium for Health (mMARCH) Journal for the Measurement of Physical Behaviour, 6(1): 37-44.

Authors:Wei Guo [aut, cre], Andrew Leroux [aut], Vadim Zipunnikov [aut], Kathleen Merikangas [aut]

mMARCH.AC_2.9.4.0.tar.gz
mMARCH.AC_2.9.4.0.tar.gz(r-4.5-noble)mMARCH.AC_2.9.4.0.tar.gz(r-4.4-noble)
mMARCH.AC_2.9.4.0.tgz(r-4.4-emscripten)mMARCH.AC_2.9.4.0.tgz(r-4.3-emscripten)
mMARCH.AC.pdf |mMARCH.AC.html
mMARCH.AC/json (API)

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

Peer review:

Bug tracker:https://github.com/weiguonimh/mmarch.ac/issues

Uses libs:
  • openjdk– OpenJDK Java runtime, using Hotspot JIT

3.11 score 26 scripts 270 downloads 29 exports 170 dependencies

Last updated 4 months agofrom:a292a6164d. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 22 2024
R-4.5-linuxNOTENov 22 2024

Exports:ActCosinor_long2ActCosinor2ActExtendCosinor_long2ActExtendCosinor2bin_data2create.shelldata.imputationDataShrinkfragmentation_long2fragmentation2get_mean_sd_hourggir.datatransformggir.summaryIS_long2IS2IV_long2IV2jive.predict2makeSleepDataMatrixmMARCH.AC.maincallPAfunpheno.plotRA_long2RA2SVDmiss2Time_long2Time2Tvol2wear_flag

Dependencies:abindaccelerometryActCRActFragashbackportsbase64encbitbit64bitopsbootbslibcachemcellrangercheckmatecliclustercodetoolscolorspacecommonmarkcosinorcosinor2cowplotcpp11crayoncubaturedata.tableDBIdenseFLMMdeSolvedigestdoParalleldplyrdvmiscevaluatefansifarverfastmapfdafdsFNNfontawesomeforeachforeignFormulafsgamm4genericsGGIRGGIRreadggplot2glueGPArotationgridExtragrpreggtablehdrcdehexViewhighrHmischmshtmlTablehtmltoolshtmlwidgetshttpuvineqirrisobanditeratorsjquerylibjsonlitekableExtrakernlabKernSmoothknitrkslabelinglaterlatticelifecyclelme4locfitlpSolvelubridatemagicmagrittrMASSmatlabMatrixmatrixStatsmclustmemoisemgcvmimeminpack.lmminqamitoolsmnormtmulticoolmunsellmvtnormnlmenloptrnnetnumDerivpbspcaPPpillarpkgconfigpracmaprettyunitsprogresspromisespsychpurrrR.methodsS3R.ooR.utilsR6rainbowrappdirsrbenchmarkRColorBrewerRcppRcppArmadilloRcppEigenRCurlread.gt3xreadxlrefundrematchrJavarlangRLRsimrmarkdownrpartrstudioapisassscalesshinysignalsourcetoolsstringistringrsurveysurvivalsvglitesystemfontstabtibbletidyrtidyselecttimechangetinytextzdbunisensRutf8vctrsviridisviridisLitevroomwithrxfunxlsxxlsxjarsXMLxml2xtableyamlzoo

mMARCH.AC: An Open-Source R/R-Markdown Package for Processing of Accelerometry Data with GGIR in Motor Activity Research Consortium for Health (mMARCH)

Rendered frommMARCH.AC.Rmdusingknitr::rmarkdownon Nov 22 2024.

Last update: 2023-08-16
Started: 2022-08-22

Readme and manuals

Help Manual

Help pageTopics
Cosinor Model for Circadian Rhythmicity for the Whole DatasetActCosinor_long2
Cosinor Model for Circadian RhythmicityActCosinor2
Cosinor Model for Circadian Rhythmicity for the Whole DatasetActExtendCosinor_long2
Extended Cosinor Model for Circadian RhythmicityActExtendCosinor2
Bin data into longer windowsbin_data2
Create a template shell script of mMARCH.ACcreate.shell
Data imputation for the cleaned data with annotationdata.imputation
Annotating the merged data for all accelerometer files in the GGIR outputDataShrink
Fragmentation Metrics for Whole Datasetfragmentation_long2
Fragmentation Metricsfragmentation2
get subject average of time variablesget_mean_sd_hour
Transform the data and merge all accelerometer files in the GGIR outputggir.datatransform
Description of all accelerometer files in the GGIR outputggir.summary
Interdaily Statbility for the Whole DatasetIS_long2
Interdaily StatbilityIS2
Intradaily Variability for the Whole DatasetIV_long2
Intradaily VariabilityIV2
Modified jive.predict function (package: r.jive)jive.predict2
Make a sleep matrix based on the sleep onset and wake up timemakeSleepDataMatrix
Main Call for Data Processing after Runing GGIR for Accelerometer DatamMARCH.AC.maincall
Timne Metrics for Whole DatasetPAfun
View phenotype variablespheno.plot
Relative Amplitude for the Whole DatsetRA_long2
Relative AmplitudeRA2
Modified SVDmiss function (package SpatioTemporal)SVDmiss2
Timne Metrics for Whole DatasetTime_long2
Time of A Certain activity StateTime2
Total Volumen of Activity for Whole DatasetTvol2
Create Wear/Nonwear Flagswear_flag