There are substantial differences between ad-hoc analyses (be they: machine learning research, data science contests, or other demonstrations) and production worthy systems. Roughly: ad-hoc analyses have to be correct only at the moment they are run (and often once they are correct, that is the last time they are run; obviously the idea of reproducible research is an attempt to raise this standard). Production systems have to be durable: they have to remain correct as models, data, packages, users, and environments change over time.
Demonstration systems need merely glow in bright light among friends; production systems must be correct, even alone in the dark.
“Character is what you are in the dark.”
John Whorfin quoting Dwight L. Moody.
I have found: to deliver production worthy data science and predictive analytic systems, one has to develop per-team and per-project field tested recommendations and best practices. This is necessary even when, or especially when, these procedures differ from official doctrine.
What I want to do is share a single small piece of Win-Vector LLC‘s current guidance on using the
dplyr. Continue reading My advice on dplyr::mutate()
dplyr is one of the most popular
R packages. It is powerful and important. But is it in fact easily comprehensible? Continue reading Is dplyr Easily Comprehensible?
I have some more thoughts on the topic: “the part-time
R-user.” Continue reading More on “The Part-Time R-User”
When I started writing about methods for better "parametric programming" interfaces for
dplyr users in December of 2016 I encountered three divisions in the audience:
dplyr users who had such a need, and wanted such extensions.
dplyr users who did not have such a need ("we always know the column names").
dplyr users who found the then-current fairly complex "underscore" and
lazyeval system sufficient for the task.
Needing name substitution is a problem an advanced full-time
R user can solve on their own. However a part-time
R would greatly benefit from a simple, reliable, readable, documented, and comprehensible packaged solution. Continue reading Let’s Have Some Sympathy For The Part-time R User