- The user specifies their desired transform declaratively by example and in data. What one does is: work an example, and then write down what you want (we have a tutorial on this here).
- The transform systems can print what a transform is going to do. This makes reasoning about data transforms much easier.
- The transforms, as they themselves are written as data, can be easily shared between systems (such as R and Python).
We also have two really nifty articles on the theory and methods:
Please give it a try!
This is the material I recently presented at the January 2017 BARUG Meetup.
cdata is our general coordinatized data tool. It is what powers the deep learning performance graph (here demonstrated with R and Keras) that I announced a while ago.
However, cdata is much more than that.
If you work with
R and data, now is the time to check out the
cdata package. Continue reading Update on coordinatized or fluid data
Authors: John Mount and Nina Zumel
One thing we commented on is that moving data values into columns, or into a “thin” or entity/attribute/value form (often called “un-pivoting”, “stacking”, “melting” or “gathering“) is easy to explain, as the operation is a function that takes a single row and builds groups of new rows in an obvious manner. We commented that the inverse operation of moving data into rows, or the “widening” operation (often called “pivoting”, “unstacking”, “casting”, or “spreading”) is harder to explain as it takes a specific group of columns and maps them back to a single row. However, if we take extra care and factor the pivot operation into its essential operations we find pivoting can be usefully conceptualized as a simple single row to single row mapping followed by a grouped aggregation.
Please read on for our thoughts on teaching pivoting data. Continue reading Teaching pivot / un-pivot
Authors: John Mount and Nina Zumel.
It has been our experience when teaching the data wrangling part of data science that students often have difficulty understanding the conversion to and from row-oriented and column-oriented data formats (what is commonly called pivoting and un-pivoting).
Real trust and understanding of this concept doesn’t fully form until one realizes that rows and columns are inessential implementation details when reasoning about your data. Many algorithms are sensitive to how data is arranged in rows and columns, so there is a need to convert between representations. However, confusing representation with semantics slows down understanding.
In this article we will try to separate representation from semantics. We will advocate for thinking in terms of coordinatized data, and demonstrate advanced data wrangling in