dsBaseClient: DataSHIELD Client Functions

Documented in ds.rBinom

#' @title Generates Binomial distribution in the server-side 
#' @description Generates random (pseudorandom) non-negative integers from a Binomial distribution.
#' Also, \code{ds.rBinom} allows creating different vector lengths in each server. 
#' @details Creates a vector of random or pseudorandom non-negative integer values 
#' distributed with a Binomial distribution. The ds.rBinom function's arguments specify 
#' the number of trials, the success probability, the length and the seed of the output 
#' vector in each source.
#' 
#' To specify a different \code{size} in each source, you can use a character vector 
#' \code{(..., size="vector.of.sizes"...)} 
#' or the \code{datasources} parameter to create the random vector for one source at a time,
#' changing \code{size} as required.
#' The default value for \code{size = 1} which simulates binary outcomes (all observations 0 or 1).
#' 
#' To specify different \code{prob} in each source, you can use an integer or  character vector 
#' \code{(..., prob="vector.of.probs"...)} or the \code{datasources} parameter to create the random 
#' vector for one source at a time, changing \code{prob} as required.
#' 
#' If \code{seed.as.integer} is an integer 
#' e.g. 5 and there is more than one source (N) the seed is set as 5*N. 
#' For example, in the first study the seed is set as 938*1, 
#' in the second as  938*2  
#' up to 938*N in the Nth study.
#' 
#' If \code{seed.as.integer} is set as 0 all sources will start with the seed value
#' 0 and all the random number generators will, therefore, start from the same position. 
#' Besides, to use the same starting seed in all studies but do not wish it to
#' be 0, you can use \code{datasources} argument to generate the random number 
#' vectors one source at a time. 
#' 
#' Server functions called: \code{rBinomDS} and \code{setSeedDS}. 
#' @param samp.size an integer value or an integer vector that defines the length of 
#' the random numeric vector to be created in each source.
#' @param size a positive integer that specifies the number of Bernoulli trials.
#' @param prob a numeric scalar value or vector  in range 0 > prob > 1 which specifies the
#' probability of a positive response (i.e. 1 rather than 0).  
#' @param newobj a character string that provides the name for the output variable 
#' that is stored on the data servers. Default \code{rbinom.newobj}. 
#' @param seed.as.integer an integer or a NULL value which provides the 
#' random seed in each data source.
#' @param return.full.seed.as.set logical, if TRUE will return the full random number seed 
#' in each data source (a numeric vector of length 626). If FALSE it will only return the 
#' trigger seed value you have provided. Default is FALSE.
#' @param datasources a list of \code{\link{DSConnection-class}} objects obtained after login. 
#' If the \code{datasources} argument is not specified
#' the default set of connections will be used: see \code{\link{datashield.connections_default}}.
#' @return \code{ds.rBinom} returns random number vectors 
#' with a Binomial distribution for each study, 
#' taking into account the values specified in each parameter of the function.
#' The output vector is written to the server-side. 
#' If requested, it also returned to the client-side the full 626 lengths 
#' random seed vector generated in each source 
#' (see info for the argument \code{return.full.seed.as.set}).
#' 
#' @examples 
#' \dontrun{
#'   ## Version 6, for version 5 see the Wiki
#'   # Connecting to the Opal servers
#' 
#'   require('DSI')
#'   require('DSOpal')
#'   require('dsBaseClient')
#' 
#'   builder <- DSI::newDSLoginBuilder()

#'   builder$append(server = "study1", 
#'                  url = "http://192.168.56.100:8080/", 
#'                  user = "administrator", password = "datashield_test&", 
#'                  table = "CNSIM.CNSIM1", driver = "OpalDriver")
#'   builder$append(server = "study2", 
#'                  url = "http://192.168.56.100:8080/", 
#'                  user = "administrator", password = "datashield_test&", 
#'                  table = "CNSIM.CNSIM2", driver = "OpalDriver")
#'   builder$append(server = "study3",
#'                  url = "http://192.168.56.100:8080/", 
#'                  user = "administrator", password = "datashield_test&", 
#'                  table = "CNSIM.CNSIM3", driver = "OpalDriver")
#'
#'   logindata <- builder$build()
#'   
#'   # Log onto the remote Opal training servers
#'   connections <- DSI::datashield.login(logins = logindata, assign = TRUE, symbol = "D") 
#' 
#'   #Generating the vectors in the Opal servers
#'   ds.rBinom(samp.size=c(13,20,25), #the length of the vector created in each source is different
#'   size=as.character(c(10,23,5)),   #Bernoulli trials change in each source 
#'   prob=c(0.6,0.1,0.5), #Probability  changes in each source 
#'   newobj="Binom.dist", 
#'   seed.as.integer=45, 
#'   return.full.seed.as.set=FALSE,
#'   datasources=connections)   #all the Opal servers are used, in this case 3 
#'                              #(see above the connection to the servers) 
#' 
#'   ds.rBinom(samp.size=15,    
#'             size=4,          
#'             prob=0.7, 
#'             newobj="Binom.dist", 
#'             seed.as.integer=324, 
#'             return.full.seed.as.set=FALSE, 
#'             datasources=connections[2]) #only the second  Opal server is used ("study2")
#'             
#'   # Clear the Datashield R sessions and logout
#'   datashield.logout(connections) 
#' }
#' @author DataSHIELD Development Team
#' @export
ds.rBinom<-function(samp.size=1,size=0,prob=1, newobj=NULL, seed.as.integer=NULL, return.full.seed.as.set=FALSE, datasources=NULL){

##################################################################################
# look for DS connections
  if(is.null(datasources)){
    datasources <- datashield.connections_find()
  }

  # ensure datasources is a list of DSConnection-class
  if(!(is.list(datasources) && all(unlist(lapply(datasources, function(d) {methods::is(d,"DSConnection")}))))){
    stop("The 'datasources' were expected to be a list of DSConnection-class objects", call.=FALSE)
  }

  # create a name by default if user did not provide a name for the new variable
  if(is.null(newobj)){
    newobj <- "rbinom.newobj"
  }

########################
#TEST SEED PRIMING VALUE
seed.valid<-0

if(is.null(seed.as.integer)){
seed.as.text<-"NULL"
seed.valid<-1
}

if(is.numeric(seed.as.integer)){
seed.as.text<-as.character(seed.as.integer)
seed.valid<-1
}

if(seed.valid==0){
mess1<-("ERROR failed: seed.as.integer must be set as an integer [numeric] or left NULL")
return(mess1)
}

###################################################################################
#samp.size is either a numeric scalar or a numeric vector set
#by the user or derived as explained for "numobs" in the help
#for the samp.size parameter - and declared without inverted commas
#as it is a client-side vector
#size and prob are either numeric scalars, or vectors on the serverside
#which contain values that can vary from row to row of a dataset.
#Such serverside vectors must be named in inverted commas. These get
#stripped off when the vector name is passed to the serverside.

arguments.valid<-1

if(is.null(samp.size)||is.null(size)||is.null(prob)||is.null(newobj)){
arguments.valid<-0
}

if(!arguments.valid){
mess2<-("ERROR: appropriate values must be set for samp.size, size, prob, and newobj name")
return(mess2)
}

size.valid<-1
if(is.numeric(size)){
	if(size<=0){
		size.valid<-0
	}
}

if(!size.valid){
mess3<-("ERROR: size must be > 0")
return(mess3)
}

prob.valid<-1
if(is.numeric(prob)){
	if(prob<=0||prob>=1.0){
		prob.valid<-0
	}
}

if(!prob.valid){
mess4<-("ERROR: prob must lie in range 0 < prob < 1")
return(mess4)
}


###################################################################################


#######################
#SET SEED IN EACH STUDY

ssDS.obj<-list()

numsources<-length(datasources)

single.integer.seed<-NULL

for(study.id in 1:numsources){

if(is.null(seed.as.integer)){
seed.as.text<-"NULL"
}

if(is.numeric(seed.as.integer)){
seed.as.integer.study.specific<-(seed.as.integer*study.id) #if set as 0 all studies will be the same
seed.as.text<-as.character(seed.as.integer.study.specific)
single.integer.seed<-c(single.integer.seed,seed.as.integer.study.specific)
}



if(seed.as.text=="NULL"){
cat("NO SEED SET IN STUDY",study.id,"\n\n")

}
  calltext <- paste0("setSeedDS(", seed.as.text, ")")
  ssDS.obj[[study.id]] <- DSI::datashield.aggregate(datasources[study.id], as.symbol(calltext))
}
cat("\n\n")





##############################
#GENERATE PSEUDORANDOM NUMBERS

if(length(samp.size)==1){
samp.size<-rep(samp.size,numsources)
}

for(k in 1:numsources){

toAssign<-paste0("rBinomDS(",samp.size[k],",",size, ",", prob, ")")


  if(is.null(toAssign)){
    stop("Please give the name of object to assign or an expression to evaluate and assign.!\n", call.=FALSE)
  }

  # now do the business

  DSI::datashield.assign(datasources[k], newobj, as.symbol(toAssign))
 }

#############################################################################################################
#DataSHIELD CLIENTSIDE MODULE: CHECK KEY DATA OBJECTS SUCCESSFULLY CREATED                                  #
																											#
#SET APPROPRIATE PARAMETERS FOR THIS PARTICULAR FUNCTION                                                 	#
test.obj.name<-newobj																					 	#
																											#																											#
																											#
# CALL SEVERSIDE FUNCTION                                                                                	#
calltext <- call("testObjExistsDS", test.obj.name)													 	#
																											#
object.info<-DSI::datashield.aggregate(datasources, calltext)												 	#
																											#
# CHECK IN EACH SOURCE WHETHER OBJECT NAME EXISTS														 	#
# AND WHETHER OBJECT PHYSICALLY EXISTS WITH A NON-NULL CLASS											 	#
num.datasources<-length(object.info)																	 	#
																											#
																											#
obj.name.exists.in.all.sources<-TRUE																	 	#
obj.non.null.in.all.sources<-TRUE																		 	#
																											#
for(j in 1:num.datasources){																			 	#
	if(!object.info[[j]]$test.obj.exists){																 	#
		obj.name.exists.in.all.sources<-FALSE															 	#
		}																								 	#
	if(is.null(object.info[[j]]$test.obj.class) || ("ABSENT" %in% object.info[[j]]$test.obj.class)){														 	#
		obj.non.null.in.all.sources<-FALSE																 	#
		}																								 	#
	}																									 	#
																											#
if(obj.name.exists.in.all.sources && obj.non.null.in.all.sources){										 	#
																											#
	return.message<-																					 	#
    paste0("A data object <", test.obj.name, "> has been created in all specified data sources")		 	#
																											#
																											#
	}else{																								 	#
																											#
    return.message.1<-																					 	#
	paste0("Error: A valid data object <", test.obj.name, "> does NOT exist in ALL specified data sources")	#
																											#
	return.message.2<-																					 	#
	paste0("It is either ABSENT and/or has no valid content/class,see return.info above")				 	#
																											#
	return.message.3<-																					 	#
	paste0("Please use ds.ls() to identify where missing")												 	#
																											#
																											#
	return.message<-list(return.message.1,return.message.2,return.message.3)							 	#
																											#
	}																										#
																											#
	calltext <- call("messageDS", test.obj.name)															#
    studyside.message<-DSI::datashield.aggregate(datasources, calltext)											#
																											#
	no.errors<-TRUE																							#
	for(nd in 1:num.datasources){																			#
		if(studyside.message[[nd]]!="ALL OK: there are no studysideMessage(s) on this datasource"){			#
		no.errors<-FALSE																					#
		}																									#
	}																										#
																											#
																											#
	if(no.errors && !return.full.seed.as.set){																#
	validity.check<-paste0("<",test.obj.name, "> appears valid in all sources")							    #
	return(list(integer.seed.as.set.by.source=single.integer.seed,random.vector.length.by.source=samp.size, #
	            is.object.created=return.message,validity.check=validity.check))							#
	}																										#
																											#
	if(no.errors && return.full.seed.as.set){																#
	validity.check<-paste0("<",test.obj.name, "> appears valid in all sources")							    #
	return(list(full.seed.as.set=ssDS.obj,																	#
				integer.seed.as.set.by.source=single.integer.seed,random.vector.length.by.source=samp.size, #
	            is.object.created=return.message,validity.check=validity.check))							#
	}																										#
																											#
if(!no.errors){																								#
	validity.check<-paste0("<",test.obj.name,"> invalid in at least one source. See studyside.messages:")   #
	return(list(is.object.created=return.message,validity.check=validity.check,					    		#
	            studyside.messages=studyside.message))			                                            #
	}																										#
																											#
#END OF CHECK OBJECT CREATED CORECTLY MODULE															 	#
#############################################################################################################



}

#ds.rBinom