Recently my student Yingkang Xie and I have developed freqparcoord, a novel approach to the parallel coordinates method for multivariate data visualization. Our approach:

- Addresses the screen-clutter problem in parallel coordinates, by only plotting the “most typical” cases, meaning those with the highest estimated multivariate density values. This makes it easier to discern relations between variables.
- Also allows plotting the “least typical” cases, i.e. those with the lowest density values, in order to find outliers.
- Allows plotting only cases that are “local maxima” in terms of density, as a means of performing clustering.

The user has the option of specifying that the computation be done parallelized. (See http://heather.cs.ucdavis.edu/paralleldatasci.pdf for a partial draft of my book, *Parallel Computing for Data Science: with Examples from R and Beyond,* to be published by Chapman & Hall later this year. Comments welcome.) For a quick intro to **freqparcoord**, download from CRAN, and load into R. Type **?freqparcoord** and run the examples, making sure to read the comments. One of the examples, whose plot is shown below, involves baseball player data, courtesy of the UCLA Statistics Dept. Here we’ve plotted the 5 most typical lines for each position. We see that catchers tend to be shorter, heavier and older, while pitchers tend to be taller, lighter and younger.

Hi, Norm Matloff here. I’m a professor of computer science at UC Davis, and was a founding member of the UCD Dept. of Statistics. You may know my book, *The Art of R Programming* (NSP, 2011). I have some **strong views on statistics**–which you are free to call analytics, data science, machine learning or whatever your favorite term is–so I’ve decided to start this blog.

In my next posting, I’ll discuss my CRAN package with Yingkang Xie, called freqparcoord. It’s a new approach to the parallel coordinates method for visualizing multivariate data.

## Musings, useful code etc. on R and data science