linear.r

#linear.r takes raw data time series index associated with some variable 'y' and uses 1 minute interpolation and 15 minute sliding average to format data. User can comment/uncomment code blocks to disable/enable features respectively. Raw data must be located on desktop as cooling.txt, IT.txt, lighting.txt, or losses.txt

#load packages zoo and chron
   require(zoo)
   require(chron)

#set working directory
   setwd("/home/knavero/Desktop")

#read in data text file to create zoo series object
   rawCool = read.zoo("cooling.txt", FUN = as.chron, format = "%m/%d/%Y %H:%M", sep = "\t", aggregate = function(x) tail(x, 1))

   rawIT = read.zoo("IT.txt", FUN = as.chron, format = "%m/%d/%Y %H:%M", sep = "\t", aggregate = function(x) tail(x, 1))

   rawLight = read.zoo("lighting.txt", FUN = as.chron, format = "%m/%d/%Y %H:%M", sep = "\t", aggregate = function(x) tail(x, 1))

   rawLoss = read.zoo("losses.txt", FUN = as.chron, format = "%m/%d/%Y %H:%M", sep = "\t", aggregate = function(x) tail(x, 1))

#graphical parameters for page layout (nr, nc)
   #par(mfrow = c(3, 1))

#raw data plots
   #par(mar = c(2, 4, 1, 1))
   #plot(rawCool, ylim = c(0, 2500), type = "o", xlab = "", ylab = "cooling", cex.lab = 1)
   #grid(col = "darkgrey")

   #par(mar = c(2, 4, 1, 1))
   #plot(rawIT, ylim = c(0, 500), type = "o", xlab = "", ylab = "IT", cex.lab = 1)
   #grid(col = "darkgrey")

   #par(mar = c(2, 4, 1, 1))
   #plot(rawLight, ylim = c(0, 11), type = "o", xlab = "", ylab = "lighting", cex.lab = 1)
   #grid(col = "darkgrey")

   #par(mar = c(2, 4, 1, 1))
   #plot(rawLoss, ylim = c(0, 90), type = "o", xlab = "", ylab = "losses", cex.lab = 1)
   #grid(col = "darkgrey")

#calculate 1 minute increment linear interpolation
   oneMin = seq(start(rawCool), end(rawCool), by = times("00:01:00"))
   intCool = na.approx(rawCool, xout = oneMin)

   oneMin = seq(start(rawIT), end(rawIT), by = times("00:01:00"))
   intIT = na.approx(rawIT, xout = oneMin)

   oneMin = seq(start(rawLight), end(rawLight), by = times("00:01:00"))
   intLight = na.approx(rawLight, xout = oneMin)

   oneMin = seq(start(rawLoss), end(rawLoss), by = times("00:01:00"))
   intLoss = na.approx(rawLoss, xout = oneMin)

   combine = merge(intCool, intIT, intLight, intLoss)
   intSum = zoo(rowSums(combine), time(combine))

#1 minute increment linear interpolation plots
   #par(mar = c(2, 4, 1, 1))
   #plot(intCool, ylim = c(0, 2500), type ="o", xlab = "", ylab ="cooling int.", cex.lab = 1)
   #grid(col ="darkgrey")

   #par(mar = c(2, 4, 1, 1))
   #plot(intIT, ylim = c(0, 500), type ="o", xlab = "", ylab ="IT int.", cex.lab = 1)
   #grid(col ="darkgrey")

   #par(mar = c(2, 4, 1, 1))
   #plot(intLight, ylim = c(0, 11), type ="o", xlab = "", ylab ="lighting int.", cex.lab = 1)
   #grid(col ="darkgrey")

   #par(mar = c(2, 4, 1, 1))
   #plot(intLoss, ylim = c(0, 90), type ="o", xlab = "", ylab ="losses int.", cex.lab = 1)
   #grid(col ="darkgrey")

   #par(mar = c(2, 4, 1, 1))
   #plot(intSum, ylim = c(0, 3000), type ="o", xlab = "", ylab ="sum int.", cex.lab = 1)
   #grid(col ="darkgrey")

#write linear interpolated data to file
   #write.zoo(intCool, file = "intOutputCooling.txt")
   #write.zoo(intIT, file = "intOutputIT.txt")
   #write.zoo(intLight, file = "intOutputLighting.txt")
   #write.zoo(intLoss, file = "intOutputLosses.txt")
   #write.zoo(intSum, file = "intOutputSum.txt")

#calculate 15 minute rolling average/mean
   min15 = times("00:15:00")
   offset = min15*14/15

   avgCool = suppressWarnings(aggregate(intCool, trunc(time(intCool) + offset, min15), mean))
   avgIT = suppressWarnings(aggregate(intIT, trunc(time(intIT) + offset, min15), mean))
   avgLight = suppressWarnings(aggregate(intLight, trunc(time(intLight) + offset, min15), mean))
   avgLoss = suppressWarnings(aggregate(intLoss, trunc(time(intLoss) + offset, min15), mean))
   avgSum = suppressWarnings(aggregate(intSum, trunc(time(intSum) + offset, min15), mean))

#15 minute rolling average/mean plot
   #par(mar = c(2, 4, 1, 1))
   #plot(avgCool, ylim =c(0, 2500), type ="o", xlab = "", ylab ="avg cooling", cex.lab =1)
   #grid(col ="darkgrey")

   #par(mar = c(2, 4, 1, 1))
   #plot(avgIT, ylim =c(0, 500), type ="o", xlab = "", ylab ="avg IT", cex.lab =1)
   #grid(col ="darkgrey")

   #par(mar = c(2, 4, 1, 1))
   #plot(avgLight, ylim =c(0, 11), type ="o", xlab = "", ylab ="avg lighting", cex.lab =1)
   #grid(col ="darkgrey")

   #par(mar = c(2, 4, 1, 1))
   #plot(avgLoss, ylim =c(0, 90), type ="o", xlab = "", ylab ="avg losses", cex.lab =1)
   #grid(col ="darkgrey")

   #par(mar = c(2, 4, 1, 1))
   #plot(avgSum, ylim =c(0, 3000), type ="o", xlab = "", ylab ="avg sum", cex.lab =1)
   #grid(col ="darkgrey")

#write linear interpolated, 15 minute sliding mean to file
   write.zoo(avgCool, file = "finalOutputCool.txt")
   write.zoo(avgIT, file = "finalOutputIT.txt")
   write.zoo(avgLight, file = "finalOutputLight.txt")
   write.zoo(avgLoss, file = "finalOutputLoss.txt")
   write.zoo(avgSum, file = "finalOutputSum.txt")