## Rule 42 Software

Continuing (and hopefully ending) our quick series on software pathologies I would like to follow-up The Hyper Dance with “Rule 42 Software.”

Continuing (and hopefully ending) our quick series on software pathologies I would like to follow-up The Hyper Dance with “Rule 42 Software.”

A lot of machine learning, statistical, plotting, and analytics algorithms over-sell a small evil trick I call “the hyper dance.”

It looks like R is getting an official pipe operator (ref). R doesn’t work under an RFC process, so we hear about these things and they are discussed on the R-devel mailing list. I’ve written on this topic before (ref), and I have taped some new comments. This sort of […]

R is a powerful data science language because, like Matlab, numpy, and Pandas, it exposes vectorized operations. That is, a user can perform operations on hundreds (or even billions) of cells by merely specifying the operation on the column or vector of values. Of course, sometimes it takes a while […]

Slides from my PyData2019 data_algebra lightning talk are here.

Introduction rquery is a data wrangling system designed to express complex data manipulation as a series of simple data transforms. This is in the spirit of R’s base::transform(), or dplyr’s dplyr::mutate() and uses a pipe in the style popularized in R with magrittr. The operators themselves follow the selections in […]

To understand computations in R, two slogans are helpful: Everything that exists is an object. Everything that happens is a function call. John Chambers In R, the “[” array access operator is a function call. And it is one a user can re-bind to the new effect of their own […]

I was working through Kyle Miller‘s excellent note: “Tail call recursion in Python”, and decided to experiment with variations of the techniques. The idea is: one may want to eliminate use of the Python language call-stack in the case of a “tail calls” (a function call where the result is […]

We at Win-Vector LLC have some big news. We are finally porting a streamlined version of our R vtreat variable preparation package to Python. vtreat is a great system for preparing messy data for supervised machine learning. The new implementation is based on Pandas, and we are experimenting with pushing […]

Here is simple modeling problem in R. We want to fit a linear model where the names of the data columns carrying the outcome to predict (y), the explanatory variables (x1, x2), and per-example row weights (wt) are given to us as string values in variables.