Visualising The Grinder data with R

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All,

I've been working on some professional looking visualisations of The
Grinder data using the statistical analysis and plotting language R.

The R script I wrote is still very alpha, but is producing some very useful
plots already. You can find the R script on Github here (you'll have to
change some constants at the start of the script to read your single data
log file):

https://github.com/gjmulder/timeseries-analysis/blob/master/grinder_analysis.R


And some sample output plots here:

http://www.perficientur.co.uk/rgrinder/


The histogram plots are an alternative way of showing response times that
provides much more detail than simplistic requests over time plots, summary
means, and standard deviations. To add context, I've then enhanced the
response time histograms by colouring them by response states (e.g. by
failed versus successful request, by HTTP response code, or by response
length).

Once you get used to R's somewhat unusual syntax, it is very easy to
generate ad hoc plots in R and has literally added a whole new dimension to
analysing my test results. I'm thinking a stacked bar plot that breaks down
overall response time by connect time + time to first byte + rest might be
interesting as well.

Feedback and suggestions for added features would be much appreciated!

Regards,
Gary

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<div dir=3D"ltr">All,<div><br></div><div>I&#39;ve been working on some prof=
essional looking visualisations of The Grinder data using the statistical a=
nalysis and plotting language R.</div><div><br></div><div>The R script I wr=
ote is still very alpha, but is producing some very useful plots already. Y=
ou can find the R script on Github here (you&#39;ll have to change some con=
stants at the start of the script to read your single data log file):</div>=
<div><br></div><blockquote style=3D"margin:0px 0px 0px 40px;border:none;pad=
ding:0px"><div><a href=3D"https://github.com/gjmulder/timeseries-analysis/b=
lob/master/grinder_analysis.R">https://github.com/gjmulder/timeseries-analy=
sis/blob/master/grinder_analysis.R</a></div></blockquote><div><br></div><di=
v>And some sample output plots here:</div><div><br></div><blockquote style=
=3D"margin:0px 0px 0px 40px;border:none;padding:0px"><div><a href=3D"http:/=
/www.perficientur.co.uk/rgrinder/">http://www.perficientur.co.uk/rgrinder/<=
/a></div></blockquote><div><br></div><div>The histogram plots are an altern=
ative way of showing response times that provides much more detail than sim=
plistic requests over time plots, summary means, and standard deviations. T=
o add context, I&#39;ve then enhanced the response time histograms by colou=
ring them by response states (e.g. by failed versus successful request, by =
HTTP response code, or by response length).</div><div><br></div><div>Once y=
ou get used to R&#39;s somewhat unusual syntax, it is very easy to generate=
 ad hoc plots in R and has literally added a whole new dimension to analysi=
ng my test results. I&#39;m thinking a stacked bar plot that breaks down ov=
erall response time by connect time + time to first byte + rest might be in=
teresting as well.<br></div><div><br></div><div>Feedback and suggestions fo=
r added features would be much appreciated!=C2=A0<br></div><div><br></div><=
div>Regards,</div><div>Gary</div></div>

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