For the next version of the R package wrapr we are going to be removing a number of under-used functions/methods and classes. This update will likely happen in March 2020, and is the start of the wrapr 2.* series.
Most of the items being removed are different abstractions for helping with function composition. We ended up moving most of our work to category-theory based composition, so don’t think these various frameworks are needed any longer. If you have been using these items in your own projects, please reach out and we try and find a way to help you out.
Continue reading wrapr Update: Removing Some Under-Used Functions and Classes
wrapr 1.9.6 is now up on CRAN.
We unfortunately usually forget to say this. A big thank you to the staff and volunteers at CRAN.
Continue reading wrapr 1.9.6 is now up on CRAN
One reason we are developing the wrapr to/unpack methods is the following: we wanted to spruce up the R vtreat interface a bit.
Continue reading Why we wrote wrapr to/unpack
I would like to introduce an exciting feature in the upcoming 1.9.6 version of the wrapr R package: value unpacking.
Continue reading unpack Your Values in R
We’ve been getting some good uptake on our piping in
R article announcement.
The article is necessarily a bit technical. But one of its key points comes from the observation that piping into names is a special opportunity to give general objects the following personality quiz: “If you were an
R function, what function would you be?”
Continue reading “If You Were an R Function, What Function Would You Be?”
We forgot to say: R Journal Volume 10/2, December 2018 is out!
A huge thanks to the editors who work very hard to make this possible.
And big “thank you” to the editors, referees, and journal for helping improve, and for including our note on pipes in
Recently Hadley Wickham prescribed pronouncing the
magrittr pipe as “then” and using right-assignment as follows:
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.
Continue reading Playing With Pipe Notations
Composing functions and sequencing operations are core programming concepts.
Some notable realizations of sequencing or pipelining operations include:
The idea is: many important calculations can be considered as a sequence of transforms applied to a data set. Each step may be a function taking many arguments. It is often the case that only one of each function’s arguments is primary, and the rest are parameters. For data science applications this is particularly common, so having convenient pipeline notation can be a plus. An example of a non-trivial data processing pipeline can be found here.
In this note we will discuss the advanced
R pipeline operator "dot arrow pipe" and an
S4 class (
wrapr::UnaryFn) that makes working with pipeline notation much more powerful and much easier.
Continue reading Function Objects and Pipelines in R
R Tip: use inline operators for legibility.
Python feature I miss when working in
R is the convenience of
+ operator. In
+ does the right thing for some built in data types:
- It concatenates lists:
[1,2] +  is
[1, 2, 3].
- It concatenates strings:
'a' + 'b' is
And, of course, it adds numbers:
1 + 2 is
The inline notation is very convenient and legible. In this note we will show how to use a related notation
Continue reading R Tip: Use Inline Operators For Legibility
In our last note we used
wrapr::qe() to help quote expressions. In this note we will discuss quoting and code-capturing interfaces (interfaces that capture user source code) a bit more.
Continue reading Quoting Concatenate