hbGPS

R package to process GPS data collected in human behaviour with option to merge time series derived with the GGIR R package for accelerometer data. The package offers a pipeline that does:

Load GPS csv files (aim is to make this flexible to most common formats)
Account for timezone in timestamp interpretation
Signal to noise calculation in GPS data
Outlier detection and removal for speed and elevation
Distance and speed calculation
Indoor/Outdoor detection
Trip detection where it allows for breaks in trips
Merging of GGIR time series output data, this allows for flexibility to work with a wide variety of accelerometer brands and data formats
User control over key parameters

A more detailed narrative description of hbGPS can be found here.

Installation

install.packages("remotes")
remotes::install_github("wadpac/GGIR")
remotes::install_github("habitus-eu/hbGPS")

Usage

The code below shows an example of how use hbGPS in combination with R package GGIR.

Process accelerometer data with GGIR

See GGIR documentation for additional details on how to use GGIR: https://cran.r-project.org/web/packages/GGIR/vignettes/GGIR.html

If you have Raw data

library(GGIR)
GGIR(datadir = "F:/path/to/your/data/folder",
     outputdir = "F:/path/to/your/output/folder",
     mode = c(1:5),
     overwrite = TRUE,
     do.report = c(),
     windowsizes = c(5, 900, 3600),
     includedaycrit = 10,
     includenightcrit = 10,
     part5_agg2_60seconds = TRUE,
     HASPT.algo = "NotWorn",
     HASIB.algo = "NotWorn",
     HASPT.ignore.invalid = FALSE,
     threshold.mod = c(100, 120),
     boutdur.in = c(25, 30),
     ignorenonwear = FALSE,
     save_ms5rawlevels = TRUE,
     save_ms5raw_without_invalid = FALSE

If you have Count data

AccThresholds = c(100, 2500, 10000, 15000) * c(5/60) # assumes GGIR's default epoch length of 5 seconds
AccThresholds = round(AccThresholds, digits = 2)

library(GGIR)
GGIR(datadir = "F:/path/to/your/data/folder",
     outputdir = "F:/path/to/your/output/folder",
     dataFormat = "actigraph_csv",
     mode = 1:5,
     overwrite = FALSE,
     do.report = c(2),
     windowsizes = c(1, 900, 3600),
     threshold.lig = AccThresholds[1],
     threshold.mod = AccThresholds[2],
     threshold.vig = AccThresholds[3],
     extEpochData_timeformat = "%m/%d/%Y %H:%M:%S",
     do.neishabouricounts = TRUE,
     acc.metric = "NeishabouriCount_x",
     HASPT.algo = "NotWorn",
     HASIB.algo = "NotWorn",
     boutdur.mvpa = 10, # note that this can be a vector
     boutdur.in = 30, # note that this can be a vector
     boutdur.lig = 10, # note that this can be a vector
     do.visual = TRUE,
     includedaycrit = 10,
     includenightcrit = 10,
     visualreport = FALSE,
     outliers.only = FALSE,
     save_ms5rawlevels = TRUE,
     ignorenonwear = FALSE,
     HASPT.ignore.invalid = FALSE,
     save_ms5raw_without_invalid = FALSE
)

Process GPS data and integrate GGIR output

gps_file = "D:/path/to/gps/data/which/can/be/folder/withfiles/or/singlefile"
outputDir = "F:/path/to/your/output/folder"

# assumption is that GGIR has already been run specify GGIR output folder:
GGIRpath = "F:/path/to/your/GGIR/output/folder/meta/ms5.outraw"

hbGPS(gps_file = gps_file,
      outputDir = outputDir,
      idloc = 2,
      maxBreakLengthSeconds = 120,
      minTripDur = 60,
      minTripDist_m = 100,
      threshold_snr = 225,
      threshold_snr_ratio = 50,
      tz = "Australia/Perth", # timezone database name
      time_format = "%Y/%m/%d %H:%M:%S",
      GGIRpath = GGIRpath,
      outputFormat = "PALMS",
      AccThresholds = AccThresholds) # alternative is "default"

The code will store a csv file with a time series for each input gps file.

habitus-eu / hbGPS

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