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 […]

Estimated reading time: 1 minute

I’d like to share some new timings on a grouped in-place aggregation task. A client of mine was seeing some slow performance, so I decided to time a very simple abstraction of one of the steps of their workflow.

Estimated reading time: 3 minutes

I’ve been writing a lot about a category theory interpretations of data-processing pipelines and some of the improvements we feel it is driving in both the data_algebra and in rquery/rqdatatable. I think I’ve found an even better category theory re-formulation of the package, which I will describe here.

Estimated reading time: 12 minutes

In our recent note What is new for rquery December 2019 we mentioned an ugly processing pipeline that translates into SQL of varying size/quality depending on the query generator we use. In this note we try a near-relative of that query in the data_algebra.

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We have a new rquery vignette here: Working with Many Columns. This is an attempt to get back to writing about how to use the package to work with data (versus the other-day’s discussion of package design/implementation). Please check it out.

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Our goal has been to make rquery the best query generation system for R (and to make data_algebra the best query generator for Python). Lets see what rquery is good at, and what new features are making rquery better.

Estimated reading time: 10 minutes

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 […]

Estimated reading time: 17 minutes

This article introduces the data_algebra project: a data processing tool family available in R and Python. These tools are designed to transform data either in-memory or on remote databases. In particular we will discuss the Python implementation (also called data_algebra) and its relation to the mature R implementations (rquery and […]

Estimated reading time: 25 minutes

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 […]

Estimated reading time: 32 minutes

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 […]

Estimated reading time: 5 minutes