Gone Guerrill_ R on Our Data

August 16, 2010

(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.

To leave a comment for the author, please follow the link and comment on their blog: The Pith of Performance.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.


Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)