applyGGIR.R

rm(list=ls())
graphics.off()
library(GGIR)

#==================================================================
# INPUT NEEDED:
# specify file number to start and end with, fill in c() if unknown
f0 = c() #file to start with if used in serial analyses
f1 = c() #file to end with if used in serial analyses (modify accordingly, if infinite then it will process until last file)
mode= c(2)    #What part of the analysis needs to be done (options: 1,2,3,4 and 5)
dayborder = 4
studyname = "data500"  #name of study, only needed if datadir is a list of filenames
datadir = "/media/windows-share/London/data500" #Where is the raw accelerometer data? (leave as c() if you work with milestone data and mode > 1
outputdir = "/media/windows-share/London/data_500"
selectdaysfile = "/media/windows-share/London/data_500/wearcodes.csv"

# datadir = "/media/sf_VBox_Shared/London/run_05-10/raw" #Where is the raw accelerometer data? (leave as c() if you work with milestone data and mode > 1
# outputdir = "/media/sf_VBox_Shared/London/run_05-10/" #Name directory where output needs to be stored
# selectdaysfile = "/media/sf_VBox_Shared/London/run_05-10/wearcodes.csv"

#=====================================================================================
# load functions from functions folder (replace by require(GGIR) once package is updated)
stt <- Sys.time()
print(paste0("Starting at: ", stt))
#=====================================================================================
g.shell.GGIR(#=======================================
             # INPUT NEEDED:
             #-------------------------------
             # General parameters
             #-------------------------------
             mode=mode, #specify above
             datadir=datadir, #specify above
             outputdir=outputdir, #specify above
             studyname=studyname, #specify above
             f0=f0, #specify above
             f1=f1, #specify above
             overwrite = FALSE, #overwrite previous milestone data?
             do.imp=TRUE, # Do imputation? (recommended)
             idloc=1, #id location (1 = file header, 2 = filename)
             print.filename=TRUE,
             storefolderstructure = FALSE,
             selectdaysfile=selectdaysfile,
             #-------------------------------
             # Part 1 parameters:
             #-------------------------------
             # Key functions: reading file, auto-calibration, and extracting features
             windowsizes = c(5,900,3600), #Epoch length, non-wear detection resolution, non-wear detection evaluation window
             do.cal=TRUE, # Apply autocalibration? (recommended)
             do.enmo = TRUE, #Needed for physical activity analysis
             do.en=TRUE,
             do.anglez=TRUE, #Needed for sleep detection
             do.angley=TRUE,
             do.anglex=TRUE,
             do.roll_med_acc_x=TRUE,
             do.roll_med_acc_y=TRUE,
             do.roll_med_acc_z=TRUE,
             do.dev_roll_med_acc_x=TRUE,
             do.dev_roll_med_acc_y=TRUE,
             do.dev_roll_med_acc_z=TRUE,
             chunksize=1.0, #size of data chunks to be read (value = 1 is maximum)
             desiredtz = "Europe/London",
             printsummary=TRUE,
             minloadcrit=46,
             epochvalues2csv = FALSE,
             #-------------------------------
             # Part 2 parameters:
             #-------------------------------
             # Key functions: Non-wear detection, imputation, and basic descriptives
             strategy = 1, #Strategy (see tutorial for explanation)
             ndayswindow=7, #only relevant when strategy = 3
             hrs.del.start = 0, # Only relevant when strategy = 2. How many HOURS need to be ignored at the START of the measurement?
             hrs.del.end = 0, # Only relevant when strategy = 2. How many HOURS need to be ignored at the END of the measurement?
             maxdur = 2, # How many DAYS of measurement do you maximumally expect?
             includedaycrit = 0, # number of minimum valid hours in a day to attempt physical activity analysis
             L5M5window = c(0,24), #window over which to calculate L5 and M5
             M5L5res = 10, #resolution in minutes of M5 and L5 calculation
             winhr = 5, # size of M5 and L5 (5 hours by default)
             qlevels = c(), #c(c(1380/1440),c(1410/1440)), #quantiles to calculate, set value at c() if you do not want quantiles
             qwindow=c(0,24), #window over which to calculate quantiles
             ilevels = c(),#c(seq(0,400,by=50),8000), #acceleration values (metric ENMO) from which a frequency distribution needs to be derived, set value at c() if you do not want quantiles
             mvpathreshold = c(100), #MVPA (moderate and vigorous physical activity threshold
             window.summary.size = 10,
             dayborder = dayborder, # dayborder is the hour at which one day becomes the next day
             bout.metric = 4,
             closedbout=FALSE,
             #-----------------------------------
             # Report generation
             #-------------------------------
             # Key functions: Generating reports based on meta-data
             do.report=c(2), #for what parts does and report need to be generated?)
             visualreport = FALSE) 

fnsh <- Sys.time()

print(paste0("DONE. At: ", fnsh))
print(paste0("Took ", difftime(fnsh, stt, units = "mins"), " minutes"))