I have up what I think is a really neat tutorial on how to plot multiple curves on a graph in Python, using seaborn and data_algebra. It is great way to show some data shaping theory convenience functions we have developed. Please check it out.
We have a new improved version of the “how to design a cdata/data_algebra data transform” up! The original article, the Python example, and the R example have all been updated to use the new video. Please check it out!
Nina Zumel and I have a two new tutorials on fluid data wrangling/shaping. They are written in a parallel structure, with the R version of the tutorial being almost identical to the Python version of the tutorial. This reflects our opinion on the “which is better for data science R […]
Nina Zumel had a really great article on how to prepare a nice Keras performance plot using R. I will use this example to show some of the advantages of cdata record transform specifications.
This note is a simple data wrangling example worked using both the Python data_algebra package and the R cdata package. Both of these packages make data wrangling easy through he use of coordinatized data concepts (relying heavily on Codd’s “rule of access”). The advantages of data_algebra and cdata are: The […]
John Mount, Nina Zumel; Win-Vector LLC 2019-04-27 In this note we will use five real life examples to demonstrate data layout transforms using the cdata R package. The examples for this note are all demo-examples from tidyr:demo/ (current when we shared this note on 2019-04-27, removed 2019-04-28), and are mostly […]
Here is an example how easy it is to use cdata to re-layout your data. Tim Morris recently tweeted the following problem (corrected). Please will you take pity on me #rstats folks? I only want to reshape two variables x & y from wide to long! Starting with: d xa […]
As of cdata version 1.0.8 cdata implements an operator notation for data transform. The idea is simple, yet powerful.
With all of the excitement surrounding cdata style control table based data transforms (the cdata ideas being named as the “replacements” for tidyr‘s current methodology, by the tidyr authors themselves!) I thought I would take a moment to describe how they work.
We recently saw this UX (user experience) question from the tidyr author as he adapts tidyr to cdata techniques. While adopting the cdata methodology into tidyr, the terminology that he is not adopting from cdata is “unpivot_to_blocks()” and “pivot_to_rowrecs()”. One of the research ideas in the cdata package is that […]