I've been a long time, occasional, user of an open source alternative to high-end statistic packages like SAS & SPSS called "R". I recently spent some time learning an associated data exploration tool called "GGobi" and the integration with R (rggobi).
While R is a worthy tool for data summarization, stats, plotting, and cleaning, GGobi is especially useful for exploration. It features "linked plots" in which mousing over a point in one plot highlights it in all other plot windows. I'm a big fan of the scatterplot matrix (shown to right) for understanding relationships between variables and distribution shapes.
This is probably overkill for basic website stats from Google Analytics, but is very useful for more complex data. I've crafted a screencast that walks through the basics of reading a CSV and launching GGobi through R. It then goes on create a scatterplot matrix and apply a custom color scheme.
The dataset in use here is data volunteered from Firefox users about their bookmarks and history data-stores. Details of the analysis are described here.
Getting a handle on R is pretty much a pre-requisite for getting value from GGobi. There are lots of great resources online including Statmethods.net Quick-R but the essential reference action is to add R-Seek.org to your browser search box.Using Open Source Tools to Understand Your Data: R & GGobi
I've been a long time, occasional, user of an open source alternative to high-end statistic packages like SAS & SPSS called "R". I recently spent some time learning an associated data exploration tool called "GGobi" and the integration with R (rggobi).
While R is a worthy tool for data summarization, stats, plotting, and cleaning, GGobi is especially useful for exploration. It features "linked plots" in which mousing over a point in one plot highlights it in all other plot windows. I'm a big fan of the scatterplot matrix (shown to right) for understanding relationships between variables and distribution shapes.
This is probably overkill for basic website stats from Google Analytics, but is very useful for more complex data. I've crafted a screencast that walks through the basics of reading a CSV and launching GGobi through R. It then goes on create a scatterplot matrix and apply a custom color scheme.
The dataset in use here is data volunteered from Firefox users about their bookmarks and history data-stores. Details of the analysis are described here.
Getting a handle on R is pretty much a pre-requisite for getting value from GGobi. There are lots of great resources online including Statmethods.net Quick-R but the essential reference action is to add R-Seek.org to your browser search box.