R - How to transform For loop containing multiple functions

Question

I'm testing the influence of the time period on the end result of my calculations. To do this, I'm using a for loop, that is detailled below. I'm sure there is a way to use apply in such a way as to remove the need for a loop, but I can't work it out. Some assistance would be more than welcome.

  ## result vector
  HLClist<-vector()

  for(i in 1:length(TimeSpan[,1])){

  StartTime=TimeSpan[i,]
  EndTime=TimeSpan[i,]+TimeInterval

  Xbis<-Choixintervalle(X,StartTime,EndTime)
  Xtierce <- resampleDF(Xbis, Ts)
  HLC<-CalcAverage(Xtierce$Ph,Xtierce$Ti,Xtierce$Te)
  HLC<-HLC[length(HLC)]
  HLClist<-append(HLClist,HLC)
}

Where

• TimeSpan is a list that contains all the startimes (format : double), defined as folows:

  InitTime <-as.POSIXct("16/02/2014 0:00", format="%d/%m/%Y %H:%M")
  FinalTime <-as.POSIXct("16/03/2014 0:00", format="%d/%m/%Y %H:%M")
  
  TimeInterval <-144*3600
  SEndTime <- FinalTime - TimeInterval
  
  TimeSpan<-data.frame(seq(InitTime, SEndTime, by=3600))
  

• TimeInterval is the number of seconds between Start and endtime (format : double)

• X is the dataframe containing all my data:

> X
                    t   Ph Elec    Sol    Ti        Te        DHW
1     16/02/2014 0:00  612  612    0.0 22.70  4.600000     0.0000
2     16/02/2014 0:05  612  612    0.0 22.70  4.600000     0.0000
3     16/02/2014 0:10  516  516    0.0 22.79  4.600000     0.0000
4     16/02/2014 0:15  480  480    0.0 22.70  4.600000     0.0000
5     16/02/2014 0:20  540  540    0.0 22.70  4.600000     0.0000
6     16/02/2014 0:25  528  528    0.0 22.60  4.600000     0.0000
7     16/02/2014 0:30  492  492    0.0 22.60  4.600000     0.0000
8     16/02/2014 0:35  528  528    0.0 22.50  4.600000     0.0000
9     16/02/2014 0:40  492  492    0.0 22.49  4.600000     0.0000
10    16/02/2014 0:45  456  456    0.0 22.43  4.600000     0.0000

• Choixintervalle is the following function:

  Choixintervalle <-function(X,startTime=NA,endTime=NA)   {   
  
  ## set the start time if not specified   
  if(is.na(startTime)){startTime <- as.POSIXct(X[1,1], format="%d/%m/%Y %H:%M")}   
  else{startTime<-as.POSIXct(startTime, format="%d/%m/%Y %H:%M")}
  
  ## set the end time if not specified   
  if(is.na(endTime)){ endTime <- as.POSIXct(X[nrow(X),1], format="%d/%m/%Y %H:%M")}   
  else{endTime<-as.POSIXct(endTime, format="%d/%m/%Y %H:%M")}
  
  X<-X[(as.POSIXct(X$t,format="%d/%m/%Y %H:%M"))>startTime,]   
  X<-X[(as.POSIXct(X$t,format="%d/%m/%Y %H:%M"))<endTime,]   
  return(X)   
  }
  

• ResempleEDF is the folowing function

  resampleDF <- function(X,Ts,startTime = NA, endTime =NA,timeName="t",includeNA=TRUE,quantizeTime=TRUE,meanNaRm=FALSE)
  {
  ## Split into periods of length Ts, and take the mean of each period
  X[,timeName] <-as.POSIXct(X[,timeName], format="%d/%m/%Y %H:%M")-startTime
  iSplit <- as.integer(X[,timeName]) %/% Ts
  
  ## Do the resampling
  Xres <- aggregate(X, list(iSplit), mean, na.rm=meanNaRm)
  
  ## Remove the "Group" column
  Xres <- Xres[,-1]
  
  ## Convert time to POSIXct
  Corr<- as.integer(Xres[1,timeName])
  Xres[,timeName] <- startTime + as.integer(Xres[,timeName])-Corr
  
  return(Xres)
  }
  

  -CalcAverage is the following function:

  CalcAverage <- function(Q, Ti,Te) { 
  
  Solution = vector(length(Q), mode="double" )
  
  for (i in 1:length(Q)){
   Temp<-sum(Ti[1:i]-Te[1:i])
   Heat<-sum(Q[1:i])  
  
  #création du vecteur R
  Solution[i]<-Heat/Temp
  
  }
  return(Solution) 
  }