GGIR package - RDocumentation

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The code in this repository is the development version for the code in R-package GGIR which can be found here: https://cran.r-project.org/web/packages/GGIR/index.html.

Example scripts used for the Millenium Cohort Study - Accelerometer Study are in the demo folder.

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Version

Install

install.packages('GGIR')

Monthly Downloads

2,609

Version

1.4

License

LGPL (>= 2.0, < 3) | file LICENSE

Maintainer

Vincent T van Hees

Last Published

January 22nd, 2017

Functions in GGIR (1.4)

g.getmeta

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

data.calibrate

Example output from g.calibrate

g.calibrate

function to estimate calibration error and make recommendation for addressing it

g.getbout

function to calculate bouts from vector of binary classes

g.binread

function to read binary files as produced by the accelerometer named 'Genea', not to be confused with the 'GENEActiv' (see package GENEAread for this)

g.inspectfile

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

data.getmeta

Example output from g.getmeta

g.impute

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.part1

function to load and pre-process acceleration files

data.inspectfile

Example output from g.inspectfile

g.wavread

function to read .wav files as produced by the accelerometer named 'Axivity'

g.plot

function to generate a plot for quality check purposes

g.part3

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

g.part4

Labels detected sustained inactivity periods by g.part3 as either nocturnal sleep or daytime sustained inactivity

GGIR-package

A package to process multi-day raw accelerometer data

g.shell.GGIR

Shell function for analysing an accelerometer dataset.

g.part5

Merge output from physical activity and sleep analysis into one report

g.part2

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