I frequently hear good things about the R language for statistical analysis of data, but it looks as though the learning curve is steep. I'm interested to know if anyone's using R to crunch data about system performance and scalability to give greater insight into behaviour than a basic time series from a monitoring system gives. What value does R give you as a sysadmin?
R is a programming language like any other, with strengths and weaknesses. The strengths are in the depth of statistical methods implemented - so, for example, if you want to fit your system loads to a generalized autoregressive conditional heteroscedastic (I'm not making that up) time series model you can. There might be an implementation of that in Python or Perl, but I doubt its as widely-used or tested.
For me its weakness is the programming language itself - fairly irregular and quirky in places. Full of traps. If you've never used a programming language before you might take to it, if you're a competent programmer in any current language (Python, Perl, C(++), VB?) you might hate it.
If the statistical techniques and graphics exist in your favourite language of choice then I'd go for that. If you could implement them quite easily in your favourite language of choice I'd think about doing that. If you want cutting-edge stats that don't exist in other programming languages, use R.
We have looked at R as a common analysis and reporting back end for data from multiple performance test tools but unfortunately we have not had the time tom implement R for this task as we have simply been too busy performance testing servers...just not enough downtime.
Here's my take on R at least from a performance testing analytics perspective
On the test server analytics side today we use a combination of monitored metrics collected during our tests, log analysis with Microsoft logparser and a reporting engine based upon LaTeX/PSTRICKS which outputs PDF. With R we expect to move to a more structured rules based analysis of results which we should be able to automate to a high degree where today we need a lot of manual intervention on the analytical side of the house for eyeballing results and formatting the output.