[[TracNav(xQTL)]]

= Add a new R script =

* Add analysis -> Add R script
* push New
* fill in a name (eg 'myScript') and extension (just fill in 'r')
* upload through file or CSV box

== Example script ==

{{{
map <- getparameter("map",jobparams)
method <- getparameter("method",jobparams)
model <- getparameter("model",jobparams)
stepsize <- getparameter("stepsize",jobparams)
  
report(2,"LoadingCrossobject")
load(paste("./run",jobid,"/cross.RData",sep=""))
report(2,"FinishedLoading")
if(stepsize!=0){
	cross <- calc.genoprob(cross, step=stepsize)
	report(2,"FinishedMarkerImputation")
}
  
report(2,"Starting%20QTL")
if(map=="scanone"){
	results <- scanone(cross,pheno.col=item,method=method,model=model,verbose=TRUE)
}else{
	results <- mqmscan(cross,pheno.col=item,verbose=TRUE)
}
  
report(2,"Starting%20QTL%20profile%20plot")
imagefilename <- paste("P",jobid,"_",item,".fig",sep="")
xfig(file = imagefilename)
plot(results)
dev.off()

report(2,"PlotInTemp.fig")
postForm(paste(dbpath,"/uploadfile",sep=""),investigation_name=investigationname, name=imagefilename, type='InvestigationFile', file=fileUpload(filename=imagefilename), style='HTTPPOST')

report(2,"UploadedFIGtoDatabase")

ResultsToMolgenis(result=results,resultname=outname, Trait_num=(item-1),investigationname=investigationname)
report(3,"Job%20Finished")
}}}


=== Adding new / additional analysis (R scripts) ===

New R scripts can be easily added as a new analysis. To get started, take a look at Add new R script. This shows the basic example (template) of such a script. This is a most minimal job.

The script is written in R, but the execution of your code does not happen here. Instead, it will produce a file that is ran by the job execution framework. 

=== Structure of the user function ===
All options defined in the parameter set and dataset configuration screens are passed to the custom script. From then on, you are in controll and can do what you want. Parameters can be retrieved by using:

{{{
message <- getparameter("message",jobparams)
}}}

The user supplied data and parameter sets are not the only parameters passed to the userscript.
The most important one is the myanalysisfile in which code need to be generated specific for the subjob and item

{{{
dbpath - database URL 
subjob - Which subjob are we
item - whihc item does this job need to do
jobid - which analysis run do we belong to
outname - output name for file or database
jobparams - User supplied parameters
investigationname - Not used yet, but will be when we have multiple instances running
libraryloc - Location of the R libraries
}}}

There is one special object you can use: a pre builded R/qtl cross. This is available when one passes a 'genotype' and a 'phenotype' matrix to a certain analysis.

{{{
load(paste("./run",jobid,"/cross.RData"),"\n",sep="");
}}}

We can thus use R as an high level esperanto language. This enables us to execute for example Beagle or Plink one 
could create a statement like:

{{{
shell.exec("plink -job=", paste("myjob"+item));
shell.exec("beagle -job=", paste("myjob"+item));
}}}

=== Points of attention ===

The progression of the job should be reported to the database. Use 'report' to do this, this reports a statuscode and a short text describing what happened to the subjob:

{{{
report(3,\"JobFinished\")
}}}

Data produced during your custom analysis can be written back directly to the database, please look at some existing scripts to see how this is done. 
However we recommend using XGAP standard functions like: {{{find.<your_xgap_datatype>}}} or {{{add.<your_xgap_datatype>}}}