Posted on Categories Exciting Techniques, TutorialsTags , , , 1 Comment on “If You Were an R Function, What Function Would You Be?”

“If You Were an R Function, What Function Would You Be?”

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?”

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Posted on Categories AdministrativiaTags , ,

R Journal Volume 10/2, December 2018 is out!

We forgot to say: R Journal Volume 10/2, December 2018 is out!

RLogo

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 R.

Posted on Categories Coding, OpinionTags , , , ,

Playing With Pipe Notations

Recently Hadley Wickham prescribed pronouncing the magrittr pipe as “then” and using right-assignment as follows:

NewImage

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.

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Posted on Categories data science, Exciting Techniques, TutorialsTags , , , , 1 Comment on Function Objects and Pipelines in R

Function Objects and Pipelines in R

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.

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Posted on Categories Programming, TutorialsTags , , ,

R Tip: Use Inline Operators For Legibility

R Tip: use inline operators for legibility.

A Python feature I miss when working in R is the convenience of Python‘s inline + operator. In Python, + does the right thing for some built in data types:

  • It concatenates lists: [1,2] + [3] is [1, 2, 3].
  • It concatenates strings: 'a' + 'b' is 'ab'.

And, of course, it adds numbers: 1 + 2 is 3.

The inline notation is very convenient and legible. In this note we will show how to use a related notation R.

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Posted on Categories Coding, Opinion, Programming, TutorialsTags , ,

Quoting Concatenate

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.

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Posted on Categories Coding, Exciting Techniques, Programming, TutorialsTags , ,

Reusable Pipelines in R

Pipelines in R are popular, the most popular one being magrittr as used by dplyr.

This note will discuss the advanced re-usable piping systems: rquery/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 %.>%.

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Posted on Categories data science, Exciting Techniques, Programming, TutorialsTags , , , , , , , 2 Comments on Sharing Modeling Pipelines in R

Sharing Modeling 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.

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Posted on Categories Programming, TutorialsTags , , , 1 Comment on Quoting in R

Quoting in R

Many R users appear to be big fans of "code capturing" or "non standard evaluation" (NSE) interfaces. In this note we will discuss quoting and non-quoting interfaces in R.

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Posted on Categories Programming, TutorialsTags , , , 2 Comments on coalesce with wrapr

coalesce with wrapr

coalesce is a classic useful SQL operator that picks the first non-NULL value in a sequence of values.

We thought we would share a nice version of it for picking non-NA R with convenient operator infix notation wrapr::coalesce(). Here is a short example of it in action:

library("wrapr")

NA %?% 0

# [1] 0

A more substantial application is the following.

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