Posted on Categories Coding, data science, Opinion, Programming, Statistics, TutorialsTags , , , , , , , , , , 10 Comments on Non-Standard Evaluation and Function Composition in R

Non-Standard Evaluation and Function Composition in R

In this article we will discuss composing standard-evaluation interfaces (SE) and composing non-standard-evaluation interfaces (NSE) in R.

In R the package tidyeval/rlang is a tool for building domain specific languages intended to allow easier composition of NSE interfaces.

To use it you must know some of its structure and notation. Here are some details paraphrased from the major tidyeval/rlang client, the package dplyr: vignette('programming', package = 'dplyr')).

  • ":=" is needed to make left-hand-side re-mapping possible (adding yet another "more than one assignment type operator running around" notation issue).
  • "!!" substitution requires parenthesis to safely bind (so the notation is actually "(!! )", not "!!").
  • Left-hand-sides of expressions are names or strings, while right-hand-sides are quosures/expressions.

Continue reading Non-Standard Evaluation and Function Composition in R

Posted on Categories data science, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, Statistics, TutorialsTags , , , , 2 Comments on Use a Join Controller to Document Your Work

Use a Join Controller to Document Your Work

This note describes a useful replyr tool we call a "join controller" (and is part of our "R and Big Data" series, please see here for the introduction, and here for one our big data courses).

Continue reading Use a Join Controller to Document Your Work

Posted on Categories Coding, Opinion, Programming, StatisticsTags , , , 7 Comments on In defense of wrapr::let()

In defense of wrapr::let()

Saw this the other day:

Wraprvstidyeval

In defense of wrapr::let() (originally part of replyr, and still re-exported by that package) I would say:

  • let() was deliberately designed for a single real-world use case: working with data when you don’t know the column names when you are writing the code (i.e., the column names will come later in a variable). We can re-phrase that as: there is deliberately less to learn as let() is adapted to a need (instead of one having to adapt to let()).
  • The R community already has months of experience confirming let() working reliably in production while interacting with a number of different packages.
  • let() will continue to be a very specific, consistent, reliable, and relevant tool even after dpyr 0.6.* is released, and the community gains experience with rlang/tidyeval in production.

If rlang/tidyeval is your thing, by all means please use and teach it. But please continue to consider also using wrapr::let(). If you are trying to get something done quickly, or trying to share work with others: a “deeper theory” may not be the best choice.

An example follows. Continue reading In defense of wrapr::let()

Posted on Categories StatisticsTags , , , , Leave a comment on Summarizing big data in R

Summarizing big data in R

Our next "R and big data tip" is: summarizing big data.

We always say "if you are not looking at the data, you are not doing science"- and for big data you are very dependent on summaries (as you can’t actually look at everything).

Simple question: is there an easy way to summarize big data in R?

The answer is: yes, but we suggest you use the replyr package to do so.

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Posted on Categories TutorialsTags , , 11 Comments on Programming over R

Programming over R

R is a very fluid language amenable to meta-programming, or alterations of the language itself. This has allowed the late user-driven introduction of a number of powerful features such as magrittr pipes, the foreach system, futures, data.table, and dplyr. Please read on for some small meta-programming effects we have been experimenting with.

NewImage Continue reading Programming over R

Posted on Categories Exciting Techniques, Pragmatic Data Science, Programming, Statistics, TutorialsTags , , , ,

Step-Debugging magrittr/dplyr Pipelines in R with wrapr and replyr

In this screencast we demonstrate how to easily and effectively step-debug magrittr/dplyr pipelines in R using wrapr and replyr.



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Posted on Categories Programming, StatisticsTags , , ,

replyr: Get a Grip on Big Data in R

replyr is an R package that contains extensions, adaptions, and work-arounds to make remote R dplyr data sources (including big data systems such as Spark) behave more like local data. This allows the analyst to more easily develop and debug procedures that simultaneously work on a variety of data services (in-memory data.frame, SQLite, PostgreSQL, and Spark2 currently being the primary supported platforms).

Replyrs Continue reading replyr: Get a Grip on Big Data in R

Posted on Categories Coding, Opinion, StatisticsTags , , , , , , , , , 7 Comments on wrapr: for sweet R code

wrapr: for sweet R code

This article is on writing sweet R code using the wrapr package.


Wrapr
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Posted on Categories Administrativia, Programming, StatisticsTags , , , , 5 Comments on Announcing the wrapr packge for R

Announcing the wrapr packge for R

Recently Dirk Eddelbuettel pointed out that our R function debugging wrappers would be more convenient if they were available in a low-dependency micro package dedicated to little else. Dirk is a very smart person, and like most R users we are deeply in his debt; so we (Nina Zumel and myself) listened and immediately moved the wrappers into a new micro-package: wrapr.


WrapperImage: Friedensreich Hundertwasser
Continue reading Announcing the wrapr packge for R

Posted on Categories Administrativia, StatisticsTags , , , , , , 5 Comments on My recent BARUG talk: Parametric Programming in R with replyr

My recent BARUG talk: Parametric Programming in R with replyr

I want to share an edited screencast of my rehearsal for my recent San Francisco Bay Area R Users Group talk: