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?”
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
Reusable modeling pipelines are a practical idea that gets re-developed many times in many contexts.
wrapr supplies a particularly powerful pipeline notation, and a pipe-stage re-use system (notes here). We will demonstrate this with the
vtreat data preparation system.
Continue reading Sharing Modeling Pipelines in R
wrapr pipe RJournal article we used piping into
ggplot2 layers/geoms/items as an example.
Being able to use the same pipe operator for data processing steps and for
ggplot2 layering is a question that comes up from time to time (for example: Why can’t ggplot2 use %>%?). In fact the primary
ggplot2 package author wishes that
magrittr piping was the composing notation for
ggplot2 (though it is obviously too late to change).
There are some fundamental difficulties in trying to use the
magrittr pipe in such a way. In particular
magrittr looks for its own pipe by name in un-evaluated code, and thus is difficult to engineer over (though it can be hacked around). The general concept is: pipe stages are usually functions or function calls, and
ggplot2 components are objects (verbs versus nouns); and at first these seem incompatible.
wrapr dot-arrow-pipe was designed to handle such distinctions.
Let’s work an example.
Continue reading Piping into ggplot2
We are thrilled to announce our (my and Nina Zumel’s) paper on the dot-pipe has been accepted by the R-Journal!
Continue reading Dot-Pipe Paper Accepted by the R Journal!!!
There are a number of easy ways to avoid illegible code nesting problems in
In this R tip we will expand upon the above statement with a simple example.
Continue reading R Tip: Break up Function Nesting for Legibility
I have just released some simple RStudio add-ins that are great for creating keyboard shortcuts when working with pipes in R.
You can install the add-ins from here (which also includes both installation instructions and use instructions/examples).
wrapr is an R package that supplies powerful tools for writing and debugging R code.
Continue reading wrapr: R Code Sweeteners