I am not sure if it is a good or bad idea. But let’s play with it a bit, and perhaps readers can submit their experience and opinions in the comments section.
This means I can time the exact same algorithm implemented nearly identically in each of these three languages. So I can extract some comparative “apples to apples” timings. Please read on for a summary of the results.
R are popular, the most popular one being
magrittr as used by
This note will discuss the advanced re-usable piping systems:
rqdatatable operator trees and
wrapr function object pipelines. In each case we have a set of objects designed to extract extra power from the
wrapr dot-arrow pipe
This note is a comment on some of the timings shared in the dplyr-0.8.0 pre-release announcement.
The original published timings were as follows:
With performance metrics: measurements are marketing. So let’s dig in the above a bit.
In August of 2003 Thomas Lumley added
R 1.8.1. This gave
R users an explicit Lisp-style quasiquotation capability.
bquote() and quasiquotation are actually quite powerful. Professor Thomas Lumley should get, and should continue to receive, a lot of credit and thanks for introducing the concept into
bquote() is already powerful enough to build a version of
dplyr 0.5.0 with quasiquotation semantics quite close (from a user perspective) to what is now claimed in
Let’s take a look at that.
Let’s take this as an excuse to take a quick look at what happens when we try a task in both systems.