NOTE: With the growing amount of functionality in GGIR we have decided to migrate the narrative documentation to the GitHub pages of GGIR. This to ease maintenance and accessibility. Therefore, many of the sections in this vignette have been replaced by a link to their new location.

1 Introduction

1.1 What is GGIR?

GGIR is an R-package to process multi-day raw accelerometer data for physical activity and sleep research. The term raw refers to data being expressed in m/s2 or gravitational acceleration as opposed to the previous generation accelerometers which stored data in accelerometer brand specific units. The signal processing includes automatic calibration, detection of sustained abnormally high values, detection of non-wear and calculation of average magnitude of dynamic acceleration based on a variety of metrics. Next, GGIR uses this information to describe the data per recording, per day of measurement, and (optionally) per segment of a day of measurement, including estimates of physical activity, inactivity and sleep. We published an overview paper of GGIR in 2019 link.

This vignette provides a general introduction on how to use GGIR and interpret the output, additionally you can find a introduction video and a mini-tutorial on YouTube. If you want to use your own algorithms for raw data then GGIR facilitates this with it’s external function embedding feature, documented in a separate vignette: Embedding external functions in GGIR. GGIR is increasingly being used by research groups across the world. A non-exhaustive overview of academic publications related to GGIR can be found here. R package GGIR would not have been possible without the support of the contributors listed in the author list at GGIR, with specific code contributions over time since April 2016 (when GGIR development moved to GitHub) shown here.

Cite GGIR:

When you use GGIR in publications do not forget to cite it properly as that makes your research more reproducible and it gives credit to it’s developers. See paragraph on Citing GGIR for details.

1.2 Contributing, Support, and Keeping up to date

How to contribute to the code?

The development version of GGIR can be found on github, which is also where you will find guidance on how to contribute.

How can I get service and support?

GGIR is open source software and does not come with service or support guarantees. However, as user-community you can help each other via the GGIR google group or the GitHub issue tracker. Please use these public platform rather than private e-mails such that other users can learn from the conversations.

If you need dedicated support with the use of GGIR or need someone to adapt GGIR to your needs then Vincent van Hees is available as independent consultant.

Training in R essentials and GGIR We offer frequent online GGIR training courses. Check our dedicated training website with more details and the option to book your training. Do you have questions about the training or the booking process? Do not hesitate to contact us via: .

Also of interest may be the brief free R introduction tutorial.

Change log

Our log of main changes to GGIR over time can be found here.

2 Setting up your work environment

2.1 Install R and RStudio

Download and install R

Download and install RStudio

Install GGIR with its dependencies from CRAN. You can do this with one command from the console command line:

install.packages("GGIR", dependencies = TRUE)

Alternatively, to install the latest development version with the latest bug fixes use instead:

install.packages("remotes")
remotes::install_github("wadpac/GGIR")

Additionally, in some use-cases you will need to install one or multiple additional packages:

  • If you are working with Axivity, GENEActiv, or GENEA files, install the GGIRread package with install.packages("GGIRread")
  • If you are working with ActiGraph gt3x files, install the read.gt3x package with install.packages("read.gt3x")
  • If you want to derive Neishabouricounts (with do.neishabouricounts = TRUE), install the actilifecounts package with install.packages("actilifecounts")
  • If you want to derive circadian rhythm indicators using the Cosinor analysis and Extended Cosinor analysis (with cosinor = TRUE), install the ActCR package with install.packages("ActCR")

2.2 Prepare folder structure

  1. GGIR works with the following accelerometer brands and formats:
    • GENEActiv .bin
    • Axivity AX3 and AX6 .cwa
    • ActiGraph .csv and .gt3x (.gt3x only the newer format generated with firmware versions above 2.5.0. Serial numbers that start with “NEO” or “MRA” and have firmware version of 2.5.0 or earlier use an older format of the .gt3x file). Note for Actigraph users: If you want to work with .csv exports via the commercial ActiLife software then note that you have the option to export data with timestamps. Please do not do this as this causes memory issues for GGIR. To cope with the absence of timestamps GGIR will calculate timestamps from the sample frequency, the start time and start date as presented in the file header.
    • Movisens .bin files with data stored in folders. GGIR expects that each participant’s folder contains at least a file named acc.bin.
    • Any other accelerometer brand that generates csv output, see documentation for functions read.myacc.csv and argument rmc.noise in the GGIR function documentation (pdf). Note that functionality for the following file formats was part of GGIR but has been deprecated as it required a significant maintenance effort without a clear use case or community support: (1) .bin for the Genea monitor by Unilever Discover, an accelerometer that was used for some studies between 2007 and 2012) .bin, and (2) .wav files as can be exported by the Axivity Ltd OMGUI software. Please contact us if you think these data formats should be facilitated by GGIR again and if you are interested in supporting their ongoing maintenance.
  2. All accelerometer data that needs to be analysed should be stored in one folder, or subfolders of that folder.
  3. Give the folder an appropriate name, preferable with a reference to the study or project it is related to rather than just ‘data’, because the name of this folder will be used later on as an identifier of the dataset.

2.3 GGIR shell function

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

2.3.1 Key general arguments

You will probably never need to think about most of the arguments listed above, because a lot of arguments are only included to facilitate methodological studies where researchers want to have control over every little detail. See previous paragraph for links to the documentation and how to find the default value of each parameter.

The bare minimum input needed for GGIR is:

library(GGIR)
GGIR(datadir="C:/mystudy/mydata",
 outputdir="D:/myresults")

Argument datadir allows you to specify where you have stored your accelerometer data and outputdir allows you to specify where you would like the output of the analyses to be stored. This cannot be equal to datadir. If you copy paste the above code to a new R script (file ending with .R) and Source it in R(Studio) then the dataset will be processed and the output will be stored in the specified output directory.

Below we have highlighted the key arguments you may want to be aware of. We are not giving a detailed explanation, please see the package manual for that.

  • mode - which part of GGIR to run, GGIR is constructed in five parts with a sixth part under development.
  • overwrite - whether to overwrite previously produced milestone output. Between each GGIR part, GGIR stores milestone output to ease re-running parts of the pipeline.
  • idloc - tells GGIR where to find the participant ID (default: inside file header)
  • data_masking_strategy - informs GGIR how to consider the design of the experiment.
    • If data_masking_strategy is set to value 1, then check out arguments hrs.del.start and hrs.del.end.
    • If data_masking_strategy is set to value 3 or 5, then check out arguments ndayswindow, hrs.del.start and hrs.del.end.
  • maxdur - maximum number of days you expect in a data file based on the study protocol.
  • desiredtz - time zone of the experiment.
  • chunksize - a way to tell GGIR to use less memory, which can be useful on machines with limited memory.
  • includedaycrit - tell GGIR how many hours of valid data per day (midnight-midnight) is acceptable.
  • includenightcrit - tell GGIR how many hours of a valid night (noon-noon) is acceptable.
  • qwindow - argument to tell GGIR whether and how to segment the day for day-segment specific analysis.
  • mvpathreshold and boutcriter - acceleration threshold and bout criteria used for calculating time spent in MVPA (only used in GGIR part2).
  • epochvalues2csv - to export epoch level magnitude of acceleration to a csv files (in addition to already being stored as RData file)
  • dayborder - to decide whether the edge of a day should be other than midnight.
  • iglevels - argument related to intensity gradient method proposed by A. Rowlands.
  • do.report - specify reports that need to be generated.
  • viewingwindow and visualreport - to create a visual report, this only works when all five parts of GGIR have successfully run. Note that the visual report was initially developed to provide something to show to study participants and not for data quality checking purposes. Over time we have improved the visual report to also be useful for QC-ing the data. however, some of the scorings as shown in the visual report are created for the visual report only and may not reflect the scorings in the main GGIR analyses as reported in the quantitative csv-reports. Most of our effort in the past 10 years has gone into making sure that the csv-report are correct, while the visualreport has mostly been a side project. This is unfortunate and we hope to find funding in the future to design a new report specifically for the purpose of QC-ing the anlayses done by GGIR.
  • maxRecordingInterval - if specified controls whether neighboring or overlapping recordings with the same participant ID and brand are appended at epoch level. This can be useful when the intention is to monitor behaviour over larger periods of time but accelerometers only allow for a few weeks of data collection. GGIR will never append or alter the raw input file, this operation is preformed on the derived data.
  • study_dates_file - if specified trims the recorded data to the first and last date in which the study took place. This is relevant for studies that started the recording several days before the accelerometers were actually worn by participants. This is used on the top of data_masking_strategy, so that it may be combined with the strategies in GGIR.

2.3.4 Published cut-points and how to use them

This section has been rewritten and moved. Please, visit the vignette Published cut-points and how to use them in GGIR for more details on the cut-points available, how to use them, and some additional reflections on the use of cut-points in GGIR.

2.3.5 Example call

If you consider all the arguments above you me may end up with a call to GGIR that could look as follows.

library(GGIR)
GGIR(mode=c(1,2,3,4,5),
      datadir="C:/mystudy/mydata",
      outputdir="D:/myresults",
      do.report=c(2,4,5),
      #=====================
      # Part 2
      #=====================
      data_masking_strategy = 1,
      hrs.del.start = 0,          hrs.del.end = 0,
      maxdur = 9,                 includedaycrit = 16,
      qwindow=c(0,24),
      mvpathreshold =c(100),
      excludefirstlast = FALSE,
      includenightcrit = 16,
      #=====================
      # Part 3 + 4
      #=====================
      def.noc.sleep = 1,
      outliers.only = TRUE,
      criterror = 4,
      do.visual = TRUE,
      #=====================
      # Part 5
      #=====================
      threshold.lig = c(30), threshold.mod = c(100),  threshold.vig = c(400),
      boutcriter = 0.8,      boutcriter.in = 0.9,     boutcriter.lig = 0.8,
      boutcriter.mvpa = 0.8, boutdur.in = c(1,10,30), boutdur.lig = c(1,10),
      boutdur.mvpa = c(1),
      includedaycrit.part5 = 2/3,
      #=====================
      # Visual report
      #=====================
      timewindow = c("WW"),
      visualreport=TRUE)

Once you have used GGIR and the output directory (outputdir) will be filled with milestone data and results.

2.3.6 Configuration file

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

3 Time for action: How to run your analysis?

3.1 From the R console on your own desktop/laptop

Create an R-script and put the GGIR call in it. Next, you can source the R-script with the source function in R:

source("pathtoscript/myshellscript.R")

or use the Source button in RStudio if you use RStudio.

3.2 In a cluster

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

3.3 Processing time

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

4 Inspecting the results

GGIR generates the following types of output. - csv-spreadsheets with all the variables you need for physical activity, sleep and circadian rhythm research - Pdfs with on each page a low resolution plot of the data per file and quality indicators - R objects with milestone data - Pdfs with a visual summary of the physical activity and sleep patterns as identified (see example below)