## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
library(knitr)
## ----pdfplot, echo=F, out.width="700px", out.height="700px"------------------
include_graphics("Figure1.workflow.pdf")
## ----eval=FALSE---------------------------------------------------------------
# install.packages("mMARCH.AC", dependencies = TRUE)
## ----eval=FALSE---------------------------------------------------------------
# library(mMARCH.AC)
# create.shell()
## ----eval=FALSE---------------------------------------------------------------
#
# argv = commandArgs(TRUE);
# print(argv)
# print(paste("length=",length(argv),sep=""))
# mode<-as.numeric(argv[1])
# print(c("mode =", mode))
# # (Note) Please remove the above lines if you are running this within R console
# # instead of submitting jobs to a cluster.
#
# #########################################################################
# # (user-define 1) you need to redefine this according different study!!!!
# #########################################################################
# # example 1
# filename2id.1<-function(x) unlist(strsplit(x,"\\."))[1]
#
# # example 2 (use csv file =c("filename","ggirID"))
# filename2id.2<-function(x) {
# d<-read.csv("./mMARCH.AC/inst/extdata/example/filename2id.csv",head=1,stringsAsFactors=F)
# y1<-which(d[,"filename"]==x)
# if (length(y1)==0) stop(paste("Missing ",x," in filename2id.csv file",sep=""))
# if (length(y1)>=1) y2<-d[y1[1],"newID"]
# return(as.character(y2))
# }
#
#
# #########################################################################
# # main call
# #########################################################################
#
# mMARCH.AC.shell<-function(mode,filename2id=NULL){
#
# library(mMARCH.AC)
# packageVersion("mMARCH.AC")
# # ?mMARCH.AC.maincall # run help to see all argumengts
#
# #########################################################################
# # (user-define 2) Fill in parameters of your ggir output
# ##########################################################################
#
# currentdir =
# studyname =
# bindir =
# outputdir =
# setwd(currentdir)
#
# rmDup=FALSE # keep all subjects in mMARCH.AC
# PA.threshold=c(50,100,400)
# part5FN="WW_L50M100V400_T5A5"
# epochIn = 5
# epochOut = 60
# use.cluster = FALSE
# log.multiplier = 9250
# QCdays.alpha = 7
# QChours.alpha = 16
# QCnights.feature.alpha = c(0,0,0,0)
# DoubleHour= "average"
# QC.sleepdur.avg=NULL
# QC.nblocks.sleep.avg=NULL
# useIDs.FN=NULL
# Rversion="R"
# desiredtz="US/Eastern"
# RemoveDaySleeper=FALSE
# NfileEachBundle=20
# holidayFN=NULL
# trace=FALSE
# #########################################################################
# # remove duplicate sample IDs for plotting and feature extraction
# #########################################################################
# if (mode==3 & rmDup){
# # step 1: read ./summary/*remove_temp.csv file (output of mode=2)
# keep.last<-TRUE #keep the latest visit for each sample
# sumdir<-paste(currentdir,"/summary",sep="")
# setwd(sumdir)
# inFN<-paste(studyname,"_samples_remove_temp.csv",sep="")
# useIDs.FN<-paste(sumdir,"/",studyname,"_samples_remove.csv",sep="")
#
# #########################################################################
# # (user-define 3 as rmDup=TRUE) create useIDs.FN file
# #########################################################################
# # step 2: create the ./summary/*remove.csv file manually or by R commands
# d<-read.csv(inFN,head=1,stringsAsFactors=F)
# d<-d[order(d[,"Date"]),]
# d<-d[order(d[,"newID"]),]
# d[which(is.na(d[,"newID"])),]
# S<-duplicated(d[,"newID"],fromLast=keep.last) #keep the last copy for nccr
# d[S,"duplicate"]<-"remove"
# write.csv(d,file=useIDs.FN,row.names=F)
#
# }
#
# #########################################################################
# # call afterggir
# #########################################################################
#
# setwd(currentdir)
# mMARCH.AC.maincall(mode=mode,
# useIDs.FN=useIDs.FN,
# currentdir=currentdir,
# studyname=studyname,
# bindir=bindir,
# outputdir=outputdir,
# epochIn=epochIn,
# epochOut=epochOut,
# log.multiplier=log.multiplier,
# use.cluster=use.cluster,
# QCdays.alpha=QCdays.alpha,
# QChours.alpha=QChours.alpha,
# QCnights.feature.alpha=QCnights.feature.alpha,
# DoubleHour= DoubleHour,
# QC.sleepdur.avg=QC.sleepdur.avg,
# QC.nblocks.sleep.avg=QC.nblocks.sleep.avg,
# Rversion=Rversion,
# filename2id=filename2id,
# PA.threshold=PA.threshold,
# desiredtz=desiredtz,
# RemoveDaySleeper=RemoveDaySleeper,
# part5FN=part5FN,
# NfileEachBundle=NfileEachBundle,
# holidayFN=holidayFN,
# trace=trace)
#
# }
# #########################################################################
# mMARCH.AC.shell(mode)
# #########################################################################
#
# # Note: mMARCH.AC.shell(mode)
# # mode = 0 : creat sw/Rmd file
# # mode = 1 : data transform using cluster or not
# # mode = 2 : summary
# # mode = 3 : clean
# # mode = 4 : impu
#
## ----eval=FALSE---------------------------------------------------------------
# mMARCH.AC.shell(mode,filename2id)
## ----eval=FALSE---------------------------------------------------------------
# #!/bin/bash
# #
# #$ -cwd
# #$ -j y
# #$ -S /bin/bash
# source ~/.bash_profile
# module load R ;
# R --no-save --no-restore --args < studyname_module0.maincall.R 0
# R --no-save --no-restore --args < studyname_module0.maincall.R 1
# R --no-save --no-restore --args < studyname_module0.maincall.R 2
# R --no-save --no-restore --args < studyname_module0.maincall.R 3
# R --no-save --no-restore --args < studyname_module0.maincall.R 4
#
# R -e "rmarkdown::render('module5_studyname_Data_process_report.Rmd' )"
# R -e "rmarkdown::render('module6_studyname_NonWear.report.Rmd' )"
# R -e "rmarkdown::render('module7a_studyname_calculate_newfeatures.Rmd' )"
# R -e "rmarkdown::render('module7b_studyname_merge_GGIRfeatures.Rmd' )"
# R -e "rmarkdown::render('module7c_studyname_runJIVE.Rmd' )"
# R -e "rmarkdown::render('module7d_studyname_calculate_WD_WE_avg_features.Rmd' )"
#
#
## ----echo=F-------------------------------------------------------------------
input<-rbind(c("timestamp ","ENMO ","anglez "),
c("2017-11-30T00:00:00+0100",8e-04,-32.5758),
c("2017-11-30T00:00:05+0100",0.0198,-25.5726),
c("2017-11-30T00:00:10+0100",0.0177,3.7972),
c("2017-11-30T00:00:15+0100",0.0118,6.7154),
c("2017-11-30T00:00:20+0100",0.0106,10.0357),
c("2017-11-30T00:00:25+0100",0.0341,21.0143),
c("2017-11-30T00:00:30+0100",0.1708,19.5008),
c("......","......","......"),
c("2017-11-30T23:59:55+0100",0.1504,-0.596))
output<-rbind(c( "Date","0:00:00","0:00:05","0:00:10","0:00:15","0:00:20","0:00:25","0:00:30","......","23:59:55"),
c( "11/30/2017","8.00E-04",0.0198,0.0177,0.0118,0.0106,0.0341,0.1708,"......",0.1504))
kable(input,format = "html")
## ----echo=F-------------------------------------------------------------------
kable(output)
## ----s1,echo=F----------------------------------------------------------------
library(xlsx)
library(knitr)
library(kableExtra)
feaFN<-system.file("template", "features.dictionary.xlsx", package = "mMARCH.AC")
#feaFN<-"/data/guow4/project0/GGIR/postGGIR/postGGIR_compile/v2/mMARCH.AC/inst/template/features.dictionary.xlsx"
dict<-read.xlsx(feaFN,head=1,sheetName="dictionary",stringsAsFactors=F)
dict.SL<-dict[which(dict[,"Domain"]=="SL"),c("Variable","Description","level","Source")]
dict.PA<-dict[which(dict[,"Domain"]=="PA"),c("Variable","Description","level","Source")]
dict.CR<-dict[which(dict[,"Domain"]=="CR"),c("Variable","Description","level","Source")]
row.names(dict.SL)<-NULL
row.names(dict.PA)<-NULL
row.names(dict.CR)<-NULL
kable(dict.SL) %>%
kable_styling(bootstrap_options = c("striped", "hover"))
## ----s2,echo=F----------------------------------------------------------------
kable(dict.PA) %>%
kable_styling(bootstrap_options = c("striped", "hover"))
## ----s3,echo=F----------------------------------------------------------------
kable(dict.CR) %>%
kable_styling(bootstrap_options = c("striped", "hover"))
## ----s4,eval=FALSE,include=FALSE----------------------------------------------
# d1<-read.xlsx("mMARCH.AC.output.description.xlsx",sheetName="output.format")
#
# cd /data/guow4/project0/GGIR/postGGIR/postGGIR_compile/v2/mMARCH.AC/vignettes
# R -e "rmarkdown::render('mMARCH.AC.Rmd' )"