# Enhanced headroom plot in R

Imported from a Blogger post I wrote in 2008. Code shared on GitHub

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 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.

> 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)

}

}

}

*Originally published at **http://perfcap.blogspot.com**.*