(This article was first published on

**The Pith of Performance**, and kindly contributed to R-bloggers)Here’s a summary of some things we learnt about applying R to computer performance and capacity planning data in the GDAT Class last week.

- Neural nets pkg
`nnet`applied to CPU performance data in the Ripley and Venables book (see Section 8.10). - How to do stacked plots that Jim calls “spark plots.”
- Jim told us that ggplot has a nice GUI but considerably slower than using the base plot routines.
- Use of
`POSIXct`to convert timestamps. - Handling multi-line headers.
- Handling multi-word fields in headers.
- To make
`getwd()`like the UNIX shell command:`pwd<-function(){cat(getwd())}`. - Think of
`lapply`as a vectorized for-loop. - Calculating confidence intervals, which David explained earlier in the week, is available as the CI function in gmodels pkg on CRAN.
- Fourier Transform Your Data. This was done using Mathematica but the same thing can be accomplished with the fftw pkg on CRAN.
- VAMOOS your data.

If you want to learn things like this, then consider putting this GDAT class on your calendar for next year.

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