default-header
Home GGIR training materials

GGIR training materials

In this page you will find all the materials as needed for the GGIR training.

Both the standard and private GGIR training assume basic knowledge about R. See our short R tutorial to check that your understanding of R is sufficient.

 

Example data for assignments

The assignment for each session is detailed in the slides (see above).

You can use the data as provided in the link below. These data files are all collected on the wrist. Make sure you unzip the folder after downloading.

Alternatively, if you a your own raw accelerometer data files with matching sleeplog you could use those. However, when participating in the standard training we advise working with the data as provided here as we may not have time during the training to troubleshoot problems you run into with your own data.

 

Example data (zipped-folder 369 MB with 4 accelerometer files and a sleeplog file)

Where to find documentation?

Main GGIR documentation

The best starting point to search for information are the GGIR github-pages.

Parameter documentation

Once you are a bit familiar with GGIR you will likely need to look up the documentation for specific parameters, for this you can go to the parameters page which is part of the above mentioned GitHub pages. However, if you do not have internet access you can also type ?GGIR in the RStudio console window and press Enter. Next, the RStudio Help window will show you a more technical description of the central GGIR function. By scrolling down to the Details section or by using the search box in the middle you can search for specific parameters.

What about the CRAN pages?

When you search for GGIR documentation online it can be that you arrive at https://cran.r-project.org/web/packages/GGIR/index.html. This is the official index page for GGIR on CRAN and it contains some of the information you also find in the GGIR github-pages. However, as a new GGIR user it is advised to focus on the GitHub-pages.

How to get help if the documentation does not answer your question?

During the GGIR training you can ask us questions in the Zoom chat or via email. Outside the training we prefer that you ask your questions in a public place. A public discussion enables others to learn from our answers, it reduces the chance that we have to answer the same question twice, it increases the chance that someone else can answer your question when we are not available, and it gives recognition for the voluntary time we put into helping GGIR users outside the training.

Where to ask questions/report problems?

  • The GGIR google group. Please note that this also works as an email list: Messages sent will be delivered to list members Inbox and deleting a message from the online platform does not delete it from their inbox. So, if you post something and regret it then please send a follow-up message to indicate that you no longer need help.
  • If you are familiar with GitHub then consider GGIR GitHub issue tracker.

Keep in mind that the people answering it are doing this voluntary, so it is important that you attempt to write an effective message.

What constitutes a good help request?

  • A short, clear and concise description of your problem.
  • Include steps to reproduce the behavior, where you need to assess what information might be relevant:
    • sensor brand
    • data format, e.g. .bin, .csv.
    • approximate recording duration.
  • Copy of R command used or config.csv file
  • Explain what you have done to investigate the issue yourself. For example, did you try processing your data based on GGIR’s default argument values first?
  • A clear and concise description of what you expected to happen.
  • Screenshots: If applicable, add screenshots to help explain your problem. Note that usually we are not only interested in seeing the error message in red, but all GGIR output to the console.
  • Example files: It often is a lot easier for us to investigate an issue if you could share an example file that allows for reproducing the problem. This does not always have to be the raw acceleration file but could also be the “.RData” file stored in output subfolder meta/basic. You will learn more about this in the GGIR training.