We have our latest note on the theory of data wrangling up here. It discusses the roles of “block records” and “row records” in the cdata data transform tool. With that and the theory of how to design transforms, we think we have a pretty complete description of the system.
As a followup to our previous post, this post goes a bit deeper into reasoning about data transforms using the cdata package. The cdata packages demonstrates the "coordinatized data" theory and includes an implementation of the "fluid data" methodology for general data re-shaping.
cdata adheres to the so-called "Rule of Representation":
Fold knowledge into data, so program logic can be stupid and robust.
In between client work, John and I have been busy working on our book, Practical Data Science with R, 2nd Edition. To demonstrate a toy example for the section I’m working on, I needed scatter plots of the petal and sepal dimensions of the iris data, like so:
I wanted a plot for petal dimensions and sepal dimensions, but I also felt that two plots took up too much space. So, I thought, why not make a faceted graph that shows both:
Except — which columns do I plot and what do I facet on?
cdata is a data manipulation package that subsumes many higher order data manipulation operations including pivot/un-pivot, spread/gather, or cast/melt. The record to record transforms are specified by drawing a table that expresses the record structure (called the “control table” and also the link between the key concepts of row-records and block-records).
What can be quickly specified and achieved using these concepts and notations is amazing and quite teachable. These transforms can be run in-memory or in remote database or big-data systems (such as Spark).
The 0.7.0 update adds local versions of the operators in addition to the Spark and database implementations. These methods should now be a bit safer for in-memory complex/annotated types such as dates and times.