Enhanced headroom plot in R

For some reason I seem to find time to write code in R when I'm on an airplane. The last two trips I made resulted in significant enhancements and debugging of the code for my headroom plot. It started off simple but it now has a lot of bells and whistles, including color coding.

Main changes: the quantile used to remove outliers now only removes outliers that exceed the 95th percentile response time by default. It keeps all the throughput values unless you use qx=True.

In each of the throughput bins used to draw the histogram, the maximum response time for that bin is now calculated and displayed as a staircase line unless you set max=False.

The set of data is now split into ranges and color coded. The times series plot is coded so you can see the split, and the scatterplot shows how those points fall. I have been plotting weekly data at one minute intervals with split=7, which looks pretty good.

I read in some data that has been extracted from vxstat into a csv format at a known URL and plotted it three ways.
I plot the first 2880 data points, picking the read data rather than write, two days at one minute intervals.

> stime <- read.csv(url("http://somewhere/vxstat_response"))
> sops <- read.csv(url("http://somewhere/vxstat_throughput"))

> names(sops)
[1] "DateTime"                             "vxstat_dg_operationsRead" 
[3] "vxstat_dg_operationsWrite"

> chp(sops[1:2880,2],stime[1:2880,2])
> chp(sops[1:2880,2],stime[1:2880,2],q=1.0)
> chp(sops[1:2880,2],stime[1:2880,2],q=1.0,splits=8)

Here is the code that generates the plot.

> chp <-function(throughput,response, q=0.95, qx=F, xl="Throughput",yl="Response",tl="Throughput Over Time",
ml="Headroom Plot", fit=T, max=T, splits=0) {
      # remove zero throughput and response values
      nonzer <- (throughput != 0) & (response != 0)  # array of true/false
      y <- response[nonzer]
      x <- throughput[nonzer]
      # remove outliers, keep response time points inside 95% by default
      if (q != 1.0) {
              quant <- (y < quantile(y,q))
              # optionally trim throughput outliers as well
              if (qx) quant <- quant & (x < quantile(x, q))
              x <- x[quant]
              y <- y[quant]
      }
      # make histograms and record end points for scaling
      xhist <- hist(x,plot=FALSE)
      yhist <- hist(y,plot=FALSE)
      xbf <- xhist$breaks[1]                          # first
      ybf <- yhist$breaks[1]                          # first
      xbl <- xhist$breaks[length(xhist$breaks)]       # last
      ybl <- yhist$breaks[length(yhist$breaks)]       # last
      xcl <- length(xhist$counts)                     # count length
      ycl <- length(yhist$counts)                     # count length
      xrange <- c(0.0,xbl)
      yrange <- c(0.0,ybl)
      xlen <- length(x)
      # make a multi-region layout
      nf <- layout(matrix(c(1,3,4,2),2,2,byrow=TRUE), c(3,1), c(1,3), TRUE)
      layout.show(nf)
      # set plot margins for throughput histogram and plot it
      par(mar=c(0,4,3,0))
      barplot(xhist$counts, axes=FALSE,
              xlim=c(xcl*0.00-xbf/((xbl-xbf)/(xcl-0.5)),xcl*1.00),
              ylim=c(0, max(xhist$counts)), space=0, main=ml)
      # set plot margins for response histogram and plot it sideways
      par(mar=c(5,0,0,1))
      barplot(yhist$counts, axes=FALSE, xlim=c(0,max(yhist$counts)),
              ylim=c(ycl*0.00-ybf/((ybl-ybf)/(ycl-0.5)),ycl*1.00),
              space=0, horiz=TRUE)
      # set plot margins for time series plot
      par(mar=c(2.5,1.7,3,1))
      plot(x, main=tl, cex.axis=0.8, cex.main=0.8, type="S")
      if (splits > 0) {
   step <- xlen/splits
   for(n in 0:(splits-1)) {
    lines((1+n*step):min((n+1)*step,xlen), x[(1+n*step):min((n+1)*step,xlen)], col=4+n)
   }
      }
      # set plot margins for main plot area
      par(mar=c(5,4,0,0))
      plot(x, y, xlim=xrange, ylim=yrange, xlab=xl, ylab=yl, pch=20)
      if (max) {
        # max curve
        b <- xhist$breaks
        i <- b[2] - b[1] # interval
        maxl <- list(y[b[1] < x & x <= (b[1]+i)])
        for(n in b[c(-1,-length(b))]) maxl <- c(maxl,list(y[n < x & x <= (n+i)]))
        #print(maxl)
        maxv <- unlist(lapply(maxl,max)) # apply max function to elements of list
        #print(maxv)
        #lines(xhist$mids,maxv,col=2)  # join the dots
        #staircase plot showing the range for each max response
        lines(rep(b,1,each=2)[2:(2*length(maxv)+1)],rep(maxv,1,each=2),col=3)
        
      }
      if (fit) {
        # fit curve, weighted to predict high throughput
        # create persistent chpfit object using <<-
        chpfit <- glm(y ~ x, inverse.gaussian, weights=as.numeric(x))
        # add fitted values to plot, sorted by throughput
        lines(x[order(x)],chpfit$fitted.values[order(x)],col=2)
      }
      if (splits > 0) {
   step <- xlen/splits
   for(n in 0:(splits-1)) {
    Sys.sleep(1)
    points(x[(1+n*step):min((n+1)*step,xlen)],y[(1+n*step):min((n+1)*step,xlen)], xlim=xrange, ylim=yrange, col=4+n)
   }
      }
}