GGIR package - RDocumentation

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Getting started:

The package vignette and this short tutorial video provide an introduction to GGIR, including: How it can be installed, Key software features, and where to get help.

Contribution guidelines:

We always welcome contributions to the package. If you want to contribute to the development of GGIR, have a look at the contribution guidelines.

Images usaged

The copyright of the GGIR logo as contained in the file vignettes/GGIR-MASTERLOGO-RGB.png lies with Accelting (Almere, The Netherlands), please contact v.vanhees@accelting.com to ask for permission to use this logo.

All other images in this repository are released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

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Version

Install

install.packages('GGIR')

Monthly Downloads

1,586

Version

3.1-4

License

Apache License (== 2.0) | file LICENSE

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/wadpac/GGIR/

Homepage

https://groups.google.com/forum/#!forum/RpackageGGIR

Maintainer

Vincent T van Hees

Last Published

September 3rd, 2024

Functions in GGIR (3.1-4)

Check default parameters

create_test_sleeplog_csv

Creates csv sleeplog file for testing purposes

create_test_acc_csv

Creates csv data file for testing purposes

cosinorAnalyses

Apply cosinor anlaysis and extended cosinor analysis

Function to revise format of user-provided logs

correctOlderMilestoneData

Corrects milestone data from g.part1 generated in older GGIR versions

Checks myfun object before it is passed to applyExtfunction

createConfigFile

Creates Config File based on variables in function GGIR environment

convertEpochData

convert external Epoch data to GGIR part 1 milestone data format

checkMilestoneFolders

Checks for existence of folders to process

detect_nonwear_clipping

Detect non-wear and clipping time in the raw accelerometer data

Extract ID from file header object

Example output from g.calibrate

Example output from g.getmeta

Time series data.frame stored by part 5

Calculates IV and IS

data.inspectfile

Example output from g.inspectfile

Extract parameters from input and add them to params

Metalong object as part of part 1 milestone data

function to estimate calibration error and make recommendation for addressing it

Generates vector of file names out of datadir input argument

g.abr.day.names

Abbreviates daynames to numbers, needed for report generation in g.plot5

Function to read activity log and make it useful for the rest of GGIR.

Function to analsyse meta-data generated by g.getmeta and g.impute

g.create.sp.mat

Converts sleep period information. Not intended for direct use

g.convert.part2.long

Convert part 2 report to long format

function to calculate bouts from vector of binary classes

Function to extract meta-data (features) from data in accelerometer file

Extract M5 and L5 from time series

g.extractheadervars

Extracts header variables from header object

Function supports g.analyse. Not intended for direct use by user.

g.fragmentation

Fragmentation metrics from time series.

Assesses whether decimals in fileheader are stored with comma or dot separated decimals

g.analyse.perday

Function supports g.analyse. Not intended for direct use by user.

Extract start time of a measurement

g.imputeTimegaps

Impute gaps in three axis raw accelerometer data

Function to identify invalid periods in the meta-data as generated by g.getmeta and to impute these invalid periods with the average of similar timepoints on other days of the measurement

g.detecmidnight

Detect all midnights in a time series

Merge output from physical activity and sleep analysis into one report

g.intensitygradient

Intensity gradient calculation

g.analyse.perfile

Function supports g.analyse. Not intended for direct use by user.

Extract metrics from acceleration signals

function to inspect accelerometer file for brand, sample frequency and header

g.part5.handle_lux_extremes

Check lux values for extremes and imputes or removes them

g.part5.fixmissingnight

Fix missing night in part 4 output

g.part5.addfirstwake

Adds first wake if it is missing in part 4 output.

g.part5.lux_persegment

Extract key lux variables per segment of the data.

g.part5.wakesleepwindows

Label wake and sleepperiod window

g.part5.onsetwaketiming

Identify wake and sleepperiod window timing

g.part5.savetimeseries

Saves part 5 time series to csv files

Generate report from milestone data produced by g.part5

function to load and pre-process acceleration files

Load and clean sleeplog information

Generate report from milestone data produced by g.part4

g.part5_analyseSegment

Analyses the time series per time segment for part 5

g.part5_initialise_ts

Initialise time series data from for part 5

Detection of sustained inactivity periods as needed for sleep detection in g.part4.

Labels detected sustained inactivity periods by g.part3 as either part of the Sleep Period Time window or not

Perform temporal pattern analyses

Wrapper function around function GGIR

g.part5.analyseRest

Analyse rest (internal function)

function to analyse and summarize pre-processed output from g.part1

Generic functiont to read large blocks of accelerometer data

g.part4_extractid

Extracts ID from filename and finds matching rows in sleeplog

function to generate a plot for quality check purposes

g.part5.definedays

Fix missing night in part 4 output

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.

Generate report from milestone data produced by g.part2

g.part5.classifyNaps

Classify Naps from identified sustained inactivty bouts

Generate sustiained inactivty bouts report

Adds the sustained inactivity bout to the ts series.

g.readtemp_movisens

Reads the temperature from movisens files.

Check whether character timestamp is in iso8601 format.

get_nw_clip_block_params

Set monitor brand specific parameters

g.report.part5_dictionary

Generate data dictionary for reports from milestone data produced by g.part5

Create plot of sustained inactivity bouts

is_this_a_dst_night

Check whether the night starting on a calendar date has DST.

Checks whether datadir is a directory or a vector with filenames

get_starttime_weekday_truncdata

Get starttime (adjusted), weekday, and adjust data accordingly.

sustiained inactivty bouts detection

Generate report from milestone data produced by g.part6

Read custom csv files with accelerometer data

part6PairwiseAggregation

part6PairwiseAggregation

Checks whether the files to process are collected with movisens accelerometers.

sustiained inactivty bouts detection

Round numeric columns and replace NA/NaN values by blank

Detects whether accelerometer is worn

parametersVignette

Builds Section for Parameters Vignette

iso8601chartime2POSIX

Convert iso8601 timestamps to POSIX timestamp

Load default parameters

getfolderstructure

Extracts folderstructure based on data directory.

updateBlocksize

Update blocksize of data to be read depending on available memory.

identify_levels

Identifies levels of behaviour for g.part5 function.

part6AlignIndividuals

part6AlignIndividuals

ShellDoc2Vignette

Extract Definition from Shell Documentation

POSIXtime2iso8601

Convert POSIX to iso8601 timestamp

A package to process multi-day raw accelerometer data

Shell function for analysing an accelerometer dataset.

applyCosinorAnalyses

Apply Cosinor Analyses to time series

Heuristic Algorithms estimating SPT window.

CalcSleepRegularityIndex

Calculates Sleep Regularity Index

Heuristic algorithms for sustiained inactivty bouts detection

Append GGIR milestone data from neighbouring or overlapping recordings

applyExtFunction

Apply external function to acceleration data.