Package: GGIR 3.3-4

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], Ian Meneghel Danilevicz [ctb], Victor Barreto Mesquita [ctb], Gaia Segantin [ctb], Xiaoyu Zong [ctb], Medical Research Council UK [cph, fnd], Accelting [cph, fnd], French National Research Agency [cph, fnd]

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

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

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

Pkgdown/docs site:https://wadpac.github.io

Datasets:

On CRAN:

Conda:

accelerometeractivity-recognitioncircadian-rhythmmovement-sensorsleep

13.68 score 136 stars 3 packages 464 scripts 4.8k downloads 81 mentions 101 exports 116 dependencies

Last updated from:0f62933d3e. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK678
source / vignettesOK612
linux-release-x86_64OK452
macos-release-arm64OK179
macos-oldrel-arm64OK163
windows-develOK236
windows-releaseOK232
windows-oldrelOK264
wasm-releaseOK509

Exports:ABIaddSplitNamesaggregateEventappendRecordsapply_cosinor_IS_IV_AnalysesCalcSleepRegularityIndexcheck_logcheck_paramsconvertEpochDatacorrectOlderMilestoneDatacosinor_IS_IV_AnalysescreateConfigFiledatadir2fnamesdetect_nonwear_clippingDFAextract_paramsextractIDfilterNonwearNightg.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.part3_alignIndexVectorsg.part3_correct_guiderg.part4g.part4_extractidg.part5g.part5_analyseSegmentg.part5_initialise_tsg.part5.addfirstwakeg.part5.addsibg.part5.analyseRestg.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_truncdatagetPart1BasicInfogetSplitNamesGGIRHASIBHASPTidentify_levelsinspect_binFile_brandis_this_a_dst_nightis.ISO8601isfilelistismovisensiso8601chartime2POSIXload_paramsmarkerButtonForRestparametersVignettepart6AlignIndividualspart6PairwiseAggregationPOSIXtime2iso8601read.myacc.csvShellDoc2VignettesplitRecordsSSPtidyup_dfupdateBlocksizevisualReport

Dependencies:ActCRbackportsbase64encbitbit64bitopsbslibcachemcellrangercheckmatecliclustercodetoolscolorspacecommonmarkcosinorcosinor2cowplotcpp11crayondata.tabledigestdoParalleldplyrevaluatefarverfastmapfontawesomeforeachforeignFormulafsgenericsGGIRreadggplot2glueGPArotationgridExtragtablehexViewhighrHmischmshtmlTablehtmltoolshtmlwidgetshttpuvineqirrisobanditeratorsjquerylibjsonliteknitrlabelinglaterlatticelifecyclelpSolvelubridatemagrittrMASSmatlabmatrixStatsmemoisemimeminpack.lmmnormtnlmennetotelpillarpkgconfigprettyunitsprogresspromisespsychpurrrR.methodsS3R.ooR.utilsR6rappdirsRColorBrewerRcppread.gt3xreadxlrematchrlangrmarkdownrpartrstudioapiS7sassscalesshinysignalsourcetoolsstringistringrtibbletidyselecttimechangetinytextzdbunisensRutf8vctrsviridisLitevroomwithrxfunXMLxtableyamlzoo

Accelerometer data processing with GGIR

Rendered fromGGIR.Rmdusingknitr::rmarkdownon Apr 16 2026.

Last update: 2025-06-23
Started: 2017-04-28

Day segment analyses with GGIR

Rendered fromTutorialDaySegmentAnalyses.Rmdusingknitr::rmarkdownon Apr 16 2026.

Last update: 2024-11-25
Started: 2021-10-20

Embedding external functions in GGIR

Rendered fromExternalFunction.Rmdusingknitr::rmarkdownon Apr 16 2026.

Last update: 2025-06-18
Started: 2020-04-30

GGIR configuration parameters

Rendered fromGGIRParameters.Rmdusingknitr::rmarkdownon Apr 16 2026.

Last update: 2026-04-16
Started: 2022-09-19

GGIR output

Rendered fromGGIRoutput.Rmdusingknitr::rmarkdownon Apr 16 2026.

Last update: 2025-11-21
Started: 2024-06-13

Nap Detection

Rendered fromNapDetection.Rmdusingknitr::rmarkdownon Apr 16 2026.

Last update: 2025-08-29
Started: 2025-08-29

Published cut-points and how to use them in GGIR

Rendered fromCutPoints.Rmdusingknitr::rmarkdownon Apr 16 2026.

Last update: 2026-01-27
Started: 2022-09-19

Reading csv files with raw data in GGIR

Rendered fromreadmyacccsv.Rmdusingknitr::rmarkdownon Apr 16 2026.

Last update: 2025-05-12
Started: 2022-05-05

Step and Cadence Analysis

Rendered fromStepCadenceAnalysis.Rmdusingknitr::rmarkdownon Apr 16 2026.

Last update: 2026-02-24
Started: 2025-12-22

Readme and manuals

Help Manual

Help pageTopics
A package to process multi-day raw accelerometer dataGGIR-package
Activity balance index (ABI)ABI
Apply Cosinor Analyses to time seriesapply_cosinor_IS_IV_Analyses
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
Detrended Fluctuation AnalysisDFA
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
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
Identify the Device Brand from a Binary Fileinspect_binFile_brand
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
Estimated self-similarity parameterSSP
Generate visualisation of time series produced by part 5.visualReport