### Execute mMARCH.AC Module 0

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

This command initiates Module 0 of the mMARCH.AC package, which handles initial data processing and setup. The module's output will be stored in the current working directory.

```R
mMARCH.AC.shell(mode=0)
```

--------------------------------

### Create mMARCH.AC Template Shell Script

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

The `create.shell()` function, part of the mMARCH.AC package, generates a template R script in the current directory. This script, typically named `_STUDYNAME_module0.maincall.R`, serves as a customizable starting point for running mMARCH.AC analyses.

```R
library(mMARCH.AC)
create.shell()
```

--------------------------------

### Install mMARCH.AC R Package

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

This R command installs the mMARCH.AC package from CRAN, automatically resolving and installing all its necessary dependencies. This ensures the package is fully functional and ready for use in your R environment.

```R
install.packages("mMARCH.AC", dependencies = TRUE)
```

--------------------------------

### Configure mMARCH.AC Parameters

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/inst/doc/quickguide.mMARCH.AC.txt

This R-like snippet shows the essential parameters that need to be filled in the 'Example_part0.maincall.R' file. These parameters define the current directory, study name, binary paths, output directory, and quality control thresholds.

```R
currentdir =  
studyname =
bindir = NULL
binfile.list = NULL
outputdir =  
QCdays.alpha = 0
QChours.alpha = 0
part5FN = 
   ......
```

--------------------------------

### Create mMARCH.AC Template File in R

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/inst/doc/quickguide.mMARCH.AC.txt

This R code snippet demonstrates how to load the mMARCH.AC library and create the initial shell template file required for the analysis. This file serves as the base for further configuration.

```R
library(mMARCH.AC)
create.shell()
```

--------------------------------

### Copy mMARCH.AC Template File

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/inst/doc/quickguide.mMARCH.AC.txt

This shell command copies the generated template file to a new study-specific file named 'Example_part0.maincall.R'. This new file will then be configured with study-specific parameters.

```Shell
cat STUDYNAME_part0.maincall.R > Example_part0.maincall.R
```

--------------------------------

### Execute mMARCH.AC Main Call Script

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/inst/doc/quickguide.mMARCH.AC.txt

These commands show how to execute the 'Example_part0.maincall.R' script from the R console or command line. Running this script generates all necessary template files for the mMARCH.AC analysis, preparing for subsequent steps.

```R
R>  call.afterggir(mode=0)
```

```R
R --no-save --no-restore --args  < Example_part0.maincall.R  0
```

--------------------------------

### mMARCH.AC Module 3: Input, Command, and Output

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

Outlines the main input file, the command to run Module 3, and the output files which include flagged raw ANGLEZ and ENMO data for cleaning, and an ID matrix.

```APIDOC
Main Input File:
./data/All_studyname_ENMO.data.csv

Command:
mMARCH.AC.shell(mode=3)

Output Folder:
./data

Output Files:
flag_All_studyname_ANGLEZ.data.Xs.csv: Adding flags for data cleaning of the raw ANGLEZ data
flag_All_studyname_ENMO.data.Xs.csv: Adding flags for data cleaning of the raw ENMO data
IDMatrix.flag_All_studyname_ENMO.data.60s.csv: ID matrix

*Xs is the epoch size to which acceleration was averaged (seconds) in GGIR output
```

--------------------------------

### mMARCH.AC Module 2: Input, Command, and Output

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

Describes the required input files, the command to execute Module 2, and the various output files generated, including a comprehensive GGIR output summary, intermediate results, plots, and a template for sample removal.

```APIDOC
Main Input Files:
+ ./data/All_studyname_ENMO.data.csv
+ GGIR results: part2, part4 and part5 (please specify \(part5FN\) in the main function)
+ GGIR raw data when bindir was specified

Command:
mMARCH.AC.shell(mode=2)

Output Folder:
./summary

Output Files:
studyname_ggir_output_summary.xlsx: Description of all accelerometer files in the GGIR output. This excel file includs 9 pages as follows, (1) List of files in the GGIR output (2) Summary of files (3) List of duplicate IDs (4) ID errors (5) Number of valid days (6) Table of number of valid/missing days (7) Missing patten (8) Frequency of the missing pattern (9) Description of all accelerometer files.
part2daysummary.info.csv: Intermediate results for description of each accelerometer file.
studyname_ggir_output_summary_plot.pdf: Some plots such as the number of valid days, which were included in the module5_studyname_Data_process_report.htm file as well.
studyname_samples_remove_temp.csv: Create studyname_samples_remove.csv file by filling "remove" in the "duplicate" column in this template. If duplicate="remove", the accelerometer files will not be used in the data analysis of module5.
```

--------------------------------

### Initialize Bootstrap Tooltips and Popovers with jQuery

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

This jQuery snippet initializes Bootstrap tooltips and popovers on elements with `data-toggle="tooltip"` and `data-toggle="popover"` attributes, respectively. It checks for the existence of the `tooltip` and `popover` functions before attempting to call them, ensuring graceful degradation if Bootstrap's JavaScript components are not loaded.

```javascript
$(document).ready(function(){ if (typeof $('\[data-toggle="tooltip"\]').tooltip === 'function') { $('\[data-toggle="tooltip"\]').tooltip(); } if ($('\[data-toggle="popover"\]').popover === 'function') { $('\[data-toggle="popover"\]').popover(); } });
```

--------------------------------

### Submit mMARCH.AC Jobs to Cluster

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/inst/doc/quickguide.mMARCH.AC.txt

This Slurm command demonstrates how to submit the 'part9_swarm.sh' script to a high-performance computing cluster. It allocates specific time and memory resources for the job, enabling parallel execution of mMARCH.AC tasks.

```Shell
sbatch --time=100:00:00 --mem=120g    part9_swarm.sh
```

--------------------------------

### GGIR Basic Data Files

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

Details the raw data files for ANGLEZ and ENMO, along with nonwear score, light, temperature, clipping, and EN data matrices, and associated plots.

```APIDOC
All_studyname_ANGLEZ.data.csv
Raw data of ANGLEZ after merge
All_studyname_ENMO.data.csv
Raw data of ENMO after merge
nonwearscore_studyname_f0_f1_Xs.csv
Data matrix of nonwearscore
nonwearscore_studyname_f0_f1_Xs.pdf
Plots for nonwearscore
plot.nonwearVSnvalidhours.csv
Nonwear data for plot
plot.nonwearVSnvalidhours.pdf
Nonwear plots
lightmean_studyname_f0_f1_Xs.csv
Data matrix of lightmean
lightpeak_studyname_f0_f1_Xs.csv
Data matrix of lightpeak
temperaturemean_studyname_f0_f1_Xs.csv
Data matrix of temperaturemean
clippingscore_studyname_f0_f1_Xs.csv
Raw data of clippingscore
EN_studyname_f0_f1_Xs.csv
Data matrix of EN

_f0 and f1 are the file index to start and finish with
_Xs is the epoch size to which acceleration was averaged (seconds) in GGIR output
```

--------------------------------

### Run mMARCH.AC Analysis Steps in R

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/inst/doc/quickguide.mMARCH.AC.txt

These R commands execute various stages of the mMARCH.AC analysis within the R environment. This includes generating different types of reports and running JIVE modules, processing the data based on the configured parameters.

```R
call.afterggir(mode=1)
call.afterggir(mode=2)
call.afterggir(mode=3)
call.afterggir(mode=4)
run part5_Example_mMARCH.AC.report.Rmd
run part6_Example_mMARCH.AC.nonwear.report.Rmd
run part7a_Example_mMARCH.AC_JIVE_1_somefeatures.Rmd
run part7b_Example_mMARCH.AC_JIVE_2_allfeatures.Rmd 
run part7c_Example_mMARCH.AC_JIVE_3_runJIVE.Rmd
run part7d_Example_mMARCH.AC_JIVE_4_somefeatures_weekday.Rmd
```

--------------------------------

### Running mMARCH.AC Modules on a Cluster

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

This Bash script demonstrates how to execute various mMARCH.AC modules and R Markdown reports in a cluster environment. It sets up the environment by sourcing bash profile and loading the R module, then sequentially runs different 'studyname_module0.maincall.R' scripts with varying 'mode' arguments, followed by rendering several R Markdown reports.

```Bash
#!/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'   )"
```

--------------------------------

### CSS Styles for Code Blocks and Syntax Highlighting

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

This extensive CSS section provides styling for `code` elements and `sourceCode` blocks, ensuring proper white-space handling, overflow management, and line numbering. It also includes detailed rules for syntax highlighting, assigning specific colors to different code elements (e.g., comments, keywords, strings) to improve code readability.

```CSS
code{white-space: pre-wrap;}
span.smallcaps{font-variant: small-caps;} span.underline{text-decoration: underline;} div.column{display: inline-block; vertical-align: top; width: 50%;} div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;} ul.task-list{list-style: none;} code { white-space: pre; } .sourceCode { overflow: visible; } pre > code.sourceCode { white-space: pre; position: relative; } pre > code.sourceCode > span { display: inline-block; line-height: 1.25; } pre > code.sourceCode > span:empty { height: 1.2em; } .sourceCode { overflow: visible; } code.sourceCode > span { color: inherit; text-decoration: inherit; } div.sourceCode { margin: 1em 0; } pre.sourceCode { margin: 0; } @media screen { div.sourceCode { overflow: auto; } } @media print { pre > code.sourceCode { white-space: pre-wrap; } pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; } } pre.numberSource code { counter-reset: source-line 0; } pre.numberSource code > span { position: relative; left: -4em; counter-increment: source-line; } pre.numberSource code > span > a:first-child::before { content: counter(source-line); position: relative; left: -1em; text-align: right; vertical-align: baseline; border: none; display: inline-block; -webkit-touch-callout: none; -webkit-user-select: none; -khtml-user-select: none; -moz-user-select: none; -ms-user-select: none; user-select: none; padding: 0 4px; width: 4em; color: #aaaaaa; } pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa; padding-left: 4px; } div.sourceCode { } @media screen { pre > code.sourceCode > span > a:first-child::before { text-decoration: underline; } } code span.al { color: #ff0000; font-weight: bold; } /* Alert */ code span.an { color: #60a0b0; font-weight: bold; font-style: italic; } /* Annotation */ code span.at { color: #7d9029; } /* Attribute */ code span.bn { color: #40a070; } /* BaseN */ code span.bu { } /* BuiltIn */ code span.cf { color: #007020; font-weight: bold; } /* ControlFlow */ code span.ch { color: #4070a0; } /* Char */ code span.cn { color: #880000; } /* Constant */ code span.co { color: #60a0b0; font-style: italic; } /* Comment */ code span.cv { color: #60a0b0; font-weight: bold; font-style: italic; } /* CommentVar */ code span.do { color: #ba2121; font-style: italic; } /* Documentation */ code span.dt { color: #902000; } /* DataType */ code span.dv { color: #40a070; } /* DecVal */ code span.er { color: #ff0000; font-weight: bold; } /* Error */ code span.ex { } /* Extension */ code span.fl { color: #40a070; } /* Float */ code span.fu { color: #06287e; } /* Function */ code span.im { } /* Import */ code span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Information */ code span.kw { color: #007020; font-weight: bold; } /* Keyword */ code span.op { color: #666666; } /* Operator */ code span.ot { color: #007020; } /* Other */ code span.pp { color: #bc7a00; } /* Preprocessor */ code span.sc { color: #4070a0; } /* SpecialChar */ code span.ss { color: #bb6688; } /* SpecialString */ code span.st { color: #4070a0; } /* String */ code span.va { color: #19177c; } /* Variable */ code span.vs { color: #4070a0; } /* VerbatimString */ code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warning */
```

--------------------------------

### mMARCH.AC Main Call Shell Script Template

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

This comprehensive R script provides a template for executing mMARCH.AC analyses, designed for both interactive R console use and cluster job submission. It includes sections for parsing command-line arguments, defining custom `filename2id` functions, setting various analysis parameters for GGIR output, and handling duplicate sample IDs before initiating the core mMARCH.AC processing.

```R
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
```

--------------------------------

### Execute mMARCH.AC Module 1

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

This command runs Module 1 of the mMARCH.AC package. It primarily processes CSV files located in the `/meta/csv` folders of GGIR output. The processed data will be saved in the `./data` directory.

```R
mMARCH.AC.shell(mode=1)
```

--------------------------------

### CSS Styles for Lightable Tables

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

This CSS snippet defines the visual presentation for tables, specifically those with 'e-paper' and 'lightable-paper' classes. It includes rules for borders, padding, vertical alignment, hover effects, and striped row backgrounds to enhance readability and user experience.

```CSS
e-paper tfoot tr:first-child td { border-top: 1px solid #00000020; }
.lightable-paper thead tr:last-child th { color: #666; vertical-align: bottom; border-bottom: 1px solid #00000020; line-height: 1.15em; padding: 10px 5px; }
.lightable-paper td { vertical-align: middle; border-bottom: 1px solid #00000010; line-height: 1.15em; padding: 7px 5px; }
.lightable-paper.lightable-hover tbody tr:hover { background-color: #F9EEC1; }
.lightable-paper.lightable-striped tbody tr:nth-child(even) { background-color: #00000008; }
.lightable-paper.lightable-striped tbody td { border: 0; }
```

--------------------------------

### mMARCH.AC Module 1 Output Files and Descriptions

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

This section describes the primary output file generated by mMARCH.AC Module 1, which summarizes the processed CSV files from GGIR output.

```APIDOC
studyname_filesummary_csvlist.csv
    Description: Summary list of processed CSV files.
```

--------------------------------

### dur_day_total_LIG_min Metric Definition

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

Total duration of day in minutes of light activity during the day.

```APIDOC
Variable: dur_day_total_LIG_min
Description: Total duration of day in minutes of light activity during the day
Level: day
Source: GGIR-part5
```

--------------------------------

### mMARCH.AC Module 0 Output Files and Descriptions

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

This section details the various R scripts, R Markdown files, and shell scripts generated or utilized by mMARCH.AC Module 0, along with their respective functionalities. These files support data transformation, report generation, feature extraction, and job submission to clusters.

```APIDOC
module1a_data.transform.R (use.cluster=TRUE, optional)
    Description: R code for data transformation and merge for every 20 files in each partition. When the number of .bin files is large ( > 1000), the data merge could take long time, user could split the job and submit the job to a cluster for parallel computing.
```

```APIDOC
module1b_data.transform.sw (use.cluster=TRUE, optional)
    Description: Submit the job to a cluster for parallel computing
```

```APIDOC
module1c_data.transform.merge.sw (use.cluster=TRUE, optional)
    Description: Merge all partitions for the ENMO and ANGLEZ data
```

```APIDOC
module5_studyname_Data_process_report.Rmd
    Description: R markdown file for generate a comprehensive report of data processing and explortatory plots.
```

```APIDOC
module6_studyname_NonWear.report.Rmd
    Description: R markdown file for generate a report of nonwear score.
```

```APIDOC
module7a_studyname_calculate_newfeatures.Rmd
    Description: Extract some features from the actigraphy data using R
```

```APIDOC
module7b_studyname_merge_GGIRfeatures.Rmd
    Description: Extract other features from the GGIR output and merge all features together
```

```APIDOC
module7c_studyname_runJIVE.Rmd
    Description: Perform JIVE Decomposition for All Features using r.jive
```

```APIDOC
module7d_studyname_calculate_WD_WE_avg_features.Rmd
    Description: Extract some weekday/weekend specific features from the actigraphy data using R
```

```APIDOC
module9_swarm.sh
    Description: shell script to submit all jobs to the cluster
```

--------------------------------

### SATP Metric Definition

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

Sedentary to active transition probabilities.

```APIDOC
Variable: SATP
Description: Sedentary to active transition probabilities.
Level: day
Source: mMARCH.AC
```

--------------------------------

### ASTP Metric Definition

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

Active to sedentary transition probabilities.

```APIDOC
Variable: ASTP
Description: Active to sedentary transition probabilities.
Level: day
Source: mMARCH.AC
```

--------------------------------

### GGIR CSV Output Files

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

Lists the primary CSV output files generated by GGIR, specifically the R data list summary.

```APIDOC
studyname_filesummary_Rdatalist.csv
```

--------------------------------

### Calling mMARCH.AC.maincall Function in R

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

This R code snippet demonstrates a comprehensive call to the `mMARCH.AC.maincall` function, which is the primary entry point for processing accelerometer data. It includes a wide range of parameters for configuring input/output directories, epoch settings, quality control thresholds, and other analysis specifics.

```R
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 Module 4: Input, Command, and Output

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

Specifies the input file, the command for Module 4, and the output file containing imputed ENMO data where missing values are filled by the average ENMO over valid days.

```APIDOC
Main Input File:
./data/flag_All_ studyname _ENMO.data.5s.csv

Command:
mMARCH.AC.shell(mode=4)

Output Folder:
./data

Output Files:
impu.flag_All_studyname_ENMO.data.60s.csv: Imputation data for the merged ENMO data, and the missing values were imputated by the average ENMO over all the valid days for each subject.
```

--------------------------------

### Module 5: Data Processing and Reporting

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

Documents the inputs, command, and outputs for Module 5, which generates a comprehensive report of data processing and exploratory plots from actigraphy data.

```APIDOC
Module 5:
  Command: run module5_studyname_Data_process_report.Rmd
  Input Files:
    - ./summary/studyname_ggir_output_summary.xlsx
    - ./summary/part24daysummary.info.csv
    - ./data/plot.nonwearVSnvalidhours.csv
    - ./data/impu.flag_All_studyname_ENMO.data.flag.epochOuts.csv
  Output Folder: ./
  Outputs:
    - module5_studyname_Data_process_report.html: A comprehensive report of data processing and explortatory plots.
```

--------------------------------

### Lightable Table Styling CSS Classes

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

This extensive CSS defines various styles for tables, prefixed with `.lightable-`. It includes styles for minimal, classic, classic-2, material, material-dark, and paper table designs. Each style provides specific formatting for borders, spacing, padding, text color, and hover/striped row effects, enhancing the visual presentation of data tables.

```css
.lightable-minimal { border-collapse: separate; border-spacing: 16px 1px; width: 100%; margin-bottom: 10px; }
.lightable-minimal td { margin-left: 5px; margin-right: 5px; }
.lightable-minimal th { margin-left: 5px; margin-right: 5px; }
.lightable-minimal thead tr:last-child th { border-bottom: 2px solid #00000050; empty-cells: hide; }
.lightable-minimal tbody tr:first-child td { padding-top: 0.5em; }
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```

--------------------------------

### Module 7b: GGIR Feature Merging

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

Describes the inputs, command, and outputs for Module 7b, which extracts and merges additional features from GGIR output, generating various aggregated and plotted data.

```APIDOC
Module 7b:
  Command: run module7b_studyname_merge_GGIRfeatures.Rmd
  Input Files:
    - GGIR: part2_summary.csv
    - GGIR: part2_daysummary.csv
    - GGIR: part4_nightsummary_sleep_cleaned.csv
    - GGIR: /results/QC/part4_nightsummary_sleep_full.csv
    - GGIR: part5_daysummary_part5FN.csv
    - module7_studyname_all_features_dictionary.xlsx
  Output Folder: ./
  Outputs:
    - module7b_studyname_merge_GGIRfeatures.html: Extract other features from the GGIR output and merge all features together
    - module7b_studyname_all_features_1_day.csv: Raw data of all features
    - module7b_studyname_all_features_2_dayclean.csv: Keep sample with valid ENMO inputs
    - module7b_studyname_all_features_2_dayclean.csv.log: Log file of each variable of module5b_studyname_all_features_2_dayclean.csv
    - plot_module7b_studyname_all_features_2_dayclean.csv.pdf: Plot of each variable of module5b_studyname_all_features_2_dayclean.csv
    - module7b_studyname_all_features_3_subject.csv: Average variable at the subject level
    - module7b_studyname_all_features_3_subject.csv.log: Log file of each variable of module5b_studyname_all_features_3_subject.csv
    - plot_module7b_studyname_all_features_3_subject.csv.pdf: Plot of each variable of module5b_studyname_all_features_3_subject.csv
    - module7b_studyname_all_features_4_subjectSD.csv: subject level SD of each feature
```

--------------------------------

### Module 7a: New Feature Calculation

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

Outlines the inputs, command, and outputs for Module 7a, which extracts new features from actigraphy data using R, including PCA at day and subject levels.

```APIDOC
Module 7a:
  Command: run module7a_studyname_calculate_newfeatures.Rmd
  Input Files:
    - ./data/impu.flag_All_studyname_ENMO.data.flag.epochOuts.csv
    - GGIR: /results/QC/part4_nightsummary_sleep_full.csv
  Output Folder: ./
  Outputs:
    - module7_studyname_all_features_dictionary.xlsx: Description of features
    - module7a_studyname_calculate_newfeatures.html: Extract some features from the actigraphy data using R
    - module7a_studyname_new_features_page1_features.csv: List of some features
    - module7a_studyname_new_features_page2_face_day_PCs.csv: Function PCA at the day level using fpca.face( )
    - module7a_studyname_new_features_page3_face_subject_PCs.csv: Function PCA at the subject level using fpca.face( )
    - module7a_studyname_new_features_page4_denseFLMM_day_PCs.csv: Function PCA at the day level using denseFLMM( )
    - module7a_studyname_new_features_page5_denseFLMM_subject_PCs.csv: Function PCA at the subject level using denseFLMM( )
```

--------------------------------

### R: Main mMARCH.AC Shell Function

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/inst/template/STUDYNAME_module0.maincall.txt

This is the primary R function, `mMARCH.AC.shell`, that orchestrates the execution of the mMARCH.AC package. It initializes package dependencies, sets various user-definable parameters for data processing, and includes conditional logic for removing duplicate sample IDs based on the 'mode' and 'rmDup' flags. Finally, it calls the core `mMARCH.AC.maincall` function with all configured parameters.

```R
mMARCH.AC.shell<-function(mode,filename2id=NULL){

library(mMARCH.AC)
library(xlsx)
packageVersion("mMARCH.AC")
#  ?mMARCH.AC.maincall  # run help to see all argumengts 

currentdir = 
studyname =
bindir = 
outputdir =  

oldwd<-getwd()
setwd(currentdir) 

rmDup=FALSE   # keep all subjects in mMARCH.AC
PA.threshold=c(40,100,400)
PA.threshold2=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
if (mode==3 & rmDup){
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="") 

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)

} 

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,
          PA.threshold2=PA.threshold2, 
          desiredtz=desiredtz,
          RemoveDaySleeper=RemoveDaySleeper,
          part5FN=part5FN,
          NfileEachBundle=NfileEachBundle,
          holidayFN=holidayFN,
          trace=trace) 

setwd(oldwd)

}
```

--------------------------------

### Module 7c: JIVE Decomposition for Features

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

Details the inputs, command, and outputs for Module 7c, which performs JIVE Decomposition on all features using r.jive, generating joint and individual structure estimates.

```APIDOC
Module 7c:
  Command: run module7b_studyname_merge_GGIRfeatures.Rmd
  Input Files:
    - module7b_studyname_all_features_3_subject.csv
    - module7b_studyname_all_features_4_subjectSD.csv
  Output Folder: ./
  Outputs:
    - module7c_studyname_runJIVE.html: Perform JIVE Decomposition for All Features using r.jive
    - module7c_studyname_jive_Decomposition.csv: Joint and individual structure estimates
    - module7c_studyname_jive_predScore.csv: PCA scores of JIVE ( missing when jive.predict failes)
```

--------------------------------

### alpha_a Metric Definition

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

Power law parameter for active bout.

```APIDOC
Variable: alpha_a
Description: Power law parameter for active bout.
Level: day
Source: mMARCH.AC
```

--------------------------------

### dur_day_MVPA_bts_1_5_min Metric Definition

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

Total duration in minutes of Moderate and Vigorous Physical Activity (MVPA) for bouts 1 to 5 minutes.

```APIDOC
Variable: dur_day_MVPA_bts_1_5_min
Description: Total duration in minutes of Moderate and Vigorous Physical Activity (MVPA) for bouts 1 to 5 minutes
Level: day
Source: GGIR-part5
```

--------------------------------

### mMARCH.AC.shell Function Modes API

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

This API documentation describes the `mMARCH.AC.shell` function and its various operational modes. Users can specify a mode to perform different tasks such as creating R Markdown files, transforming data, generating summaries, cleaning data, or performing imputation.

```APIDOC
mMARCH.AC.shell(mode: integer)
  mode: The operational mode for the shell function.
    mode = 0 : Create sw/Rmd file
    mode = 1 : Data transform (with or without cluster)
    mode = 2 : Summary generation
    mode = 3 : Data cleaning
    mode = 4 : Imputation
```

--------------------------------

### dur_day_MVPA_bts_5_10_min Metric Definition

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

Total duration in minutes of Moderate and Vigorous Physical Activity (MVPA) for bouts 5 to 10 minutes.

```APIDOC
Variable: dur_day_MVPA_bts_5_10_min
Description: Total duration in minutes of Moderate and Vigorous Physical Activity (MVPA) for bouts 5 to 10 minutes
Level: day
Source: GGIR-part5
```

--------------------------------

### R: Execute mMARCH.AC Shell with Mode

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/inst/template/STUDYNAME_module0.maincall.txt

This line executes the `mMARCH.AC.shell` function, passing the previously determined 'mode' argument. This initiates the main workflow of the mMARCH.AC package based on the specified operation mode.

```R
mMARCH.AC.shell(mode)
```

--------------------------------

### Module 7d: Weekday/Weekend Average Feature Calculation

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

Outlines the inputs, command, and outputs for Module 7d, which extracts weekday and weekend specific features from actigraphy data using R.

```APIDOC
Module 7d:
  Command: run module7d_studyname_calculate_WD_WE_avg_features.Rmd
  Input File: ./data/impu.flag_All_studyname_ENMO.data.flag.epochOuts.csv
  Output Folder: ./
  Outputs:
    - module7d_studyname_calculate_WD_WE_avg_features.html: Extract some weekday/weekend specific features from the actigraphy data using R
    - module7d_studyname_new_features_page1.csv: Perform JIVE Decomposition for All Features using r.jive
```

--------------------------------

### dur_day_total_VIG_min Metric Definition

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

Total duration of day in minutes of vigorous activity during the day.

```APIDOC
Variable: dur_day_total_VIG_min
Description: Total duration of day in minutes of vigorous activity during the day
Level: day
Source: GGIR-part5
```

--------------------------------

### alpha_r Metric Definition

Source: https://github.com/weiguonimh/mmarch.ac/blob/main/mMARCH.AC.html

Power law parameter for sedentary bout.

```APIDOC
Variable: alpha_r
Description: Power law parameter for sedentary bout.
Level: day
Source: mMARCH.AC
```