Nina and I are cleaning up websites, links, and projects. I would like to take the opportunity re-share my old genetic art project through a short demonstration video. Read more about the Genetic Art Project here.
I’d like to share a video of my old knot editing software in action. Read more about KnotEd here.
Win Vector LLC’s Dr. Nina Zumel has had great success applying y-aware methods to machine learning problems, and working out the detailed cross-validation methods needed to make y-aware procedures safe. I thought I would try our hand at y-aware neural net or deep learning methods here.
wrapr 2.0.0 is now up on CRAN. This means the := variant of unpack is now easy to install. Please give it a try!
For data science projects I recommend using source control or version control, and committing changes at a very fine level of granularity. This means checking in possibly broken code, and the possibly weak commit messages (so when working in a shared project, you may want a private branch or second […]
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.
Recently ran into something interesting in the R macros/quasi-quotation/substitution/syntax front: Romain François: “.@_lionelhenry reveals planned double curly syntax At #satRdayParis as a possible replacement, addition to !! and enquo()” It appears !! is no longer the last word in substitution (it certainly wasn’t the first).
To make getting started with rquery (an advanced query generator for R) easier we have re-worked the package README for various data-sources (including SparkR!).