GGIR（Actigraphy）技术咨询：代码查看与结果优化相关问题

Hey there! Let's break down your GGIR questions with practical, actionable solutions— I’ve spent plenty of time troubleshooting this package for accelerometer studies, so I’ve got you covered:

Getting your hands on GGIR’s complete code is straightforward, with a few options depending on what you need:

• View source for installed functions: In R, just type any GGIR function name without parentheses (e.g., g.part4) to pull up its full code. To browse all R scripts in the package, run system.file(package = "GGIR") to get the installation path, then navigate to the R folder inside— that’s where all the core logic lives.
• Grab the latest development version: GGIR’s active development happens on GitHub. Use devtools::install_github("wadpac/GGIR") to install the dev build, then in RStudio’s Packages pane, expand the GGIR entry to see every function. Click any function to view its source code instantly. If you want a local copy of the entire repo, just clone it directly.
• Check built-in docs for context: Run ?GGIR to pull up the main package documentation, which links to detailed explanations of each module, plus example code that helps clarify how everything connects.

I’ve double-checked this against GGIR’s source code and official docs— here’s the standard color mapping for those sleep plots:

• Blue: The pre-defined sleep period window (set via parameters like timewindow or your metadata’s "time in bed" specification)
• Red: Wakefulness detected by GGIR’s sleep algorithm
• Green: Sleep detected by the algorithm
• Yellow/light green: Uncertain sleep/wake status (usually when there’s too little movement data to make a confident call)

If you want to verify this yourself, look into the plot_sleep.R script in GGIR’s source code— it explicitly defines these color mappings.

Both of these tweaks are totally doable with either parameter settings or quick pre/post-processing:

• Displaying accurate subject IDs:
  • GGIR defaults to using accelerometer filenames as IDs, so if your filenames are already valid subject IDs, you’re good to go.
  • To use custom IDs from a metadata file, specify the metadatapath parameter when calling GGIR() or g.part4(). Make sure your metadata has an ID column (default is column 1; adjust with idcol if needed) that matches your filenames, and GGIR will pull those IDs into all outputs.
• Sorting output in ascending order:
  • Pre-processing fix: Before passing files to GGIR, sort your file list by ID. For example:

    file_list <- sort(list.files(path = "your_accel_data_folder", pattern = ".cwa", full.names = TRUE))
    

    Then use this sorted file_list as the datapath input— GGIR will process and output results in this order.
  • Post-processing fix: If you already have results, load the output RData file (e.g., load("part4_results.RData")) and use dplyr::arrange() to sort by ID:

    library(dplyr)
    sorted_results <- arrange(part4_results, ID)
    

  • Built-in parameter: GGIR’s main GGIR() function has a sortbyid parameter— set this to TRUE to automatically sort all outputs by subject ID in ascending order.

内容的提问来源于stack exchange，提问作者NikoSchool