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.