Package: GGIR 3.1-2

Vincent T van Hees

GGIR:Raw Accelerometer Data Analysis

A tool to process and analyse data collected with wearable raw acceleration sensors as described in Migueles and colleagues (JMPB 2019), and van Hees and colleagues (JApplPhysiol 2014; PLoSONE 2015). The package has been developed and tested for binary data from 'GENEActiv' <https://activinsights.com/>, binary (.gt3x) and .csv-export data from 'Actigraph' <https://theactigraph.com> devices, and binary (.cwa) and .csv-export data from 'Axivity' <https://axivity.com>. These devices are currently widely used in research on human daily physical activity. Further, the package can handle accelerometer data file from any other sensor brand providing that the data is stored in csv format. Also the package allows for external function embedding.

Authors:Vincent T van Hees [aut, cre], Jairo H Migueles [aut], Severine Sabia [ctb], Matthew R Patterson [ctb], Zhou Fang [ctb], Joe Heywood [ctb], Joan Capdevila Pujol [ctb], Lena Kushleyeva [ctb], Mathilde Chen [ctb], Manasa Yerramalla [ctb], Patrick Bos [ctb], Taren Sanders [ctb], Chenxuan Zhao [ctb], Medical Research Council UK [cph, fnd], Accelting [cph, fnd], French National Research Agency [cph, fnd]

GGIR_3.1-2.tar.gz
GGIR_3.1-2.tar.gz(r-4.5-noble)GGIR_3.1-2.tar.gz(r-4.4-noble)
GGIR_3.1-2.tgz(r-4.4-emscripten)GGIR_3.1-2.tgz(r-4.3-emscripten)
GGIR.pdf |GGIR.html✨
GGIR/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/wadpac/ggir/issues

Datasets:

88 exports 9 stars 2.14 score 116 dependencies 3 dependents 1.9k downloads

Last updated 3 days agofrom:41dde4065f

Exports:appendRecordsapplyCosinorAnalysesCalcSleepRegularityIndexcheck_logcheck_paramsconvertEpochDatacorrectOlderMilestoneDatacosinorAnalysescreateConfigFiledatadir2fnamesdetect_nonwear_clippingextract_paramsextractIDg.abr.day.namesg.analyseg.applymetricsg.calibrateg.conv.actlogg.create.sp.matg.detecmidnightg.dotorcommag.extractheadervarsg.fragmentationg.getboutg.getM5L5g.getmetag.getstarttimeg.imputeg.imputeTimegapsg.inspectfileg.intensitygradientg.IVISg.loadlogg.part1g.part2g.part3g.part4g.part4_extractidg.part5g.part5_analyseSegmentg.part5_initialise_tsg.part5.addfirstwakeg.part5.addsibg.part5.analyseRestg.part5.classifyNapsg.part5.definedaysg.part5.fixmissingnightg.part5.handle_lux_extremesg.part5.lux_persegmentg.part5.onsetwaketimingg.part5.savetimeseriesg.part5.wakesleepwindowsg.part6g.plotg.plot5g.readaccfileg.readtemp_movisensg.report.part2g.report.part4g.report.part5g.report.part5_dictionaryg.report.part6g.shell.GGIRg.sib.detg.sib.plotg.sib.sumg.sibreportg.weardecget_nw_clip_block_paramsget_starttime_weekday_truncdataGGIRHASIBHASPTidentify_levelsis_this_a_dst_nightis.ISO8601isfilelistismovisensiso8601chartime2POSIXload_paramsparametersVignettepart6AlignIndividualspart6PairwiseAggregationPOSIXtime2iso8601read.myacc.csvShellDoc2Vignettetidyup_dfupdateBlocksize

Dependencies:ActCRbackportsbase64encbitbit64bitopsbslibcachemcheckmatecliclustercodetoolscolorspacecommonmarkcosinorcosinor2cowplotcpp11crayondata.tabledigestdoParalleldplyrevaluatefansifarverfastmapfontawesomeforeachforeignFormulafsgenericsGGIRreadggplot2glueGPArotationgridExtragtablehexViewhighrHmischmshtmlTablehtmltoolshtmlwidgetshttpuvineqirrisobanditeratorsjquerylibjsonliteknitrlabelinglaterlatticelifecyclelpSolvelubridatemagrittrMASSmatlabMatrixmatrixStatsmemoisemgcvmimeminpack.lmmnormtmunsellnlmennetpillarpkgconfigprettyunitsprogresspromisespsychpurrrR.methodsS3R.ooR.utilsR6rappdirsRColorBrewerRcppread.gt3xrlangrmarkdownrpartrstudioapisassscalesshinysignalsourcetoolsstringistringrtibbletidyselecttimechangetinytextzdbunisensRutf8vctrsviridisviridisLitevroomwithrxfunXMLxtableyamlzoo

Accelerometer data processing with GGIR

Rendered fromGGIR.Rmdusingknitr::rmarkdownon Jul 03 2024.

Last update: 2024-07-03
Started: 2017-04-28

Day segment analyses with GGIR

Rendered fromTutorialDaySegmentAnalyses.Rmdusingknitr::rmarkdownon Jul 03 2024.

Last update: 2024-07-03
Started: 2021-10-26

Embedding external functions in GGIR

Rendered fromExternalFunction.Rmdusingknitr::rmarkdownon Jul 03 2024.

Last update: 2024-07-03
Started: 2020-05-01

GGIR configuration parameters

Rendered fromGGIRParameters.Rmdusingknitr::rmarkdownon Jul 03 2024.

Last update: 2024-06-05
Started: 2022-09-19

GGIR output

Rendered fromGGIRoutput.Rmdusingknitr::rmarkdownon Jul 03 2024.

Last update: 2024-07-03
Started: 2024-07-03

Published cut-points and how to use them in GGIR

Rendered fromCutPoints.Rmdusingknitr::rmarkdownon Jul 03 2024.

Last update: 2024-07-03
Started: 2022-09-19

Reading csv files with raw data in GGIR

Rendered fromreadmyacccsv.Rmdusingknitr::rmarkdownon Jul 03 2024.

Last update: 2024-07-03
Started: 2022-05-23

Readme and manuals

Help Manual

Help pageTopics
A package to process multi-day raw accelerometer dataGGIR-package
Apply Cosinor Analyses to time seriesapplyCosinorAnalyses
Creates csv data file for testing purposescreate_test_acc_csv
Creates csv sleeplog file for testing purposescreate_test_sleeplog_csv
Example output from g.calibratedata.calibrate
Example output from g.getmetadata.getmeta
Example output from g.inspectfiledata.inspectfile
Metalong object as part of part 1 milestone datadata.metalong
Time series data.frame stored by part 5data.ts
function to estimate calibration error and make recommendation for addressing itg.calibrate
function to calculate bouts from vector of binary classesg.getbout
Function to extract meta-data (features) from data in accelerometer fileg.getmeta
Impute gaps in three axis raw accelerometer datag.imputeTimegaps
function to inspect accelerometer file for brand, sample frequency and headerg.inspectfile
Load and clean sleeplog informationg.loadlog
function to load and pre-process acceleration filesg.part1
function to analyse and summarize pre-processed output from g.part1g.part2
Detection of sustained inactivity periods as needed for sleep detection in g.part4.g.part3
Labels detected sustained inactivity periods by g.part3 as either part of the Sleep Period Time window or notg.part4
Merge output from physical activity and sleep analysis into one reportg.part5
Analyse rest (internal function)g.part5.analyseRest
Perform temporal pattern analysesg.part6
Generate user-friendly visual report. The first part of the report summarizes important daily metrics in bar plot format. The second part of the report shows the raw data and annotations in 24-hr periods. Angle-z is shown with sleep annotations during the SPT (sleep period time) window. ENMO is shown with daytime inactivity and PA (physical activity) annotations in the lower section of each 24-hr plot. The PA annotations are based on a 10 minute bout metric and 80 of a 10 minute bout of MVPA. Vigorous PA is a short window of time above threshold.vig that is part of a bout of MVPA. Light PA is a short window of time above threshold.lig that is part of a bout of light PA.g.plot5
Generate report from milestone data produced by g.part2g.report.part2
Generate report from milestone data produced by g.part4g.report.part4
Generate report from milestone data produced by g.part5g.report.part5
Generate data dictionary for reports from milestone data produced by g.part5g.report.part5_dictionary
Generate report from milestone data produced by g.part6g.report.part6
Wrapper function around function GGIRg.shell.GGIR
Shell function for analysing an accelerometer dataset.GGIR
Check whether character timestamp is in iso8601 format.is.ISO8601
Convert iso8601 timestamps to POSIX timestampiso8601chartime2POSIX
Load default parametersload_params
part6AlignIndividualspart6AlignIndividuals
part6PairwiseAggregationpart6PairwiseAggregation
Convert POSIX to iso8601 timestampPOSIXtime2iso8601
Read custom csv files with accelerometer dataread.myacc.csv