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.
R package and training materials we emphasize the record-oriented thinking and how to design a transform control table. We now have an additional exciting new feature: control table keys.
The user can now control which columns of a
cdata control table are the keys, including now using composite keys (that is keys that are spread across more than one column). This is easiest to demonstrate with an example.
To make teaching
R quasi-quotation easier it would be nice if
R string-interpolation and quasi-quotation both used the same notation. They are related concepts. So some commonality of notation would actually be clarifying, and help teach the concepts. We will define both of the above terms, and demonstrate the relation between the two concepts.
While working on a variation of the
RcppDynProg algorithm we derived the following beautiful identity of 2 by 2 real matrices:
The superscript “top” denoting the transpose operation, the ||.||^2_2 denoting sum of squares norm, and the single |.| denoting determinant.
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.
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.
Our group has done a lot of work with non-standard calling conventions in
Our tools work hard to eliminate non-standard calling (as is the purpose of
wrapr::let()), or at least make it cleaner and more controllable (as is done in the wrapr dot pipe). And even so, we still get surprised by some of the side-effects and mal-consequences of the over-use of non-standard calling conventions in
Please read on for a recent example.
This note is just a quick follow-up to our last note on correcting the bias in estimated standard deviations for binomial experiments.