R – Win-Vector Blog

October 16, 2018October 16, 2018John MountCoding, Opinionmacros, quasiquotation, R1 Comment

Quasiquotation in R via bquote()

In August of 2003 Thomas Lumley added bquote() to R 1.8.1. This gave R and R users an explicit Lisp-style quasiquotation capability. bquote() and quasiquotation are actually quite powerful. Professor Thomas Lumley should get, and should continue to receive, a lot of credit and thanks for introducing the concept into R.

In fact bquote() is already powerful enough to build a version of dplyr 0.5.0 with quasiquotation semantics quite close (from a user perspective) to what is now claimed in tidyeval/rlang.

Let’s take a look at that.

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October 13, 2018October 13, 2018John MountProgramming, Tutorialsdot arrow, dot pipe, dotpipe, ggplot2, R, wraprLeave a comment

Piping into ggplot2

In our 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.

However, the wrapr dot-arrow-pipe was designed to handle such distinctions.

Let’s work an example.

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October 10, 2018October 10, 2018John MountOpinion, Tutorialsdata.table, R, tidyverseLeave a comment

Some R Guides: tidyverse and data.table Versions

Saghir Bashir of ilustat recently shared a nice getting started with R and tidyverse guide.

In addition they were generous enough to link to Dirk Eddelbuette’s later adaption of the guide to use data.table.

This type of cooperation and user choice is what keeps the R community vital. Please encourage it. (Heck, please insist on it!)

October 8, 2018October 16, 2018John MountCoding, Opiniondata.table, python, R13 Comments

Running the Same Task in Python and R

According to a KDD poll fewer respondents (by rate) used only R in 2017 than in 2016. At the same time more respondents (by rate) used only Python in 2017 than in 2016.

Let’s take this as an excuse to take a quick look at what happens when we try a task in both systems.

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October 6, 2018October 11, 2018John MountExciting Techniques, Practical Data Science, Pragmatic Data Science, Statistics, TutorialsA/B testing, R, sigr1 Comment

Quick Significance Calculations for A/B Tests in R

Introduction

Let’s take a quick look at a very important and common experimental problem: checking if the difference in success rates of two Binomial experiments is statistically significant. This can arise in A/B testing situations such as online advertising, sales, and manufacturing.

We already share a free video course on a Bayesian treatment of planning and evaluating A/B tests (including a free Shiny application). Let’s now take a look at the should be simple task of simply building a summary statistic that includes a classic frequentist significance.

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October 1, 2018John Mountdata science, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, Statistics, TutorialsData preparation, Machine Learning, R, vtreatLeave a comment

Modeling muti-category Outcomes With vtreat

vtreat is a powerful R package for preparing messy real-world data for machine learning. We have further extended the package with a number of features including rquery/rqdatatable integration (allowing vtreat application at scale on Apache Spark or data.table!).

In addition vtreat and can now effectively prepare data for multi-class classification or multinomial modeling.

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September 25, 2018John MountOpinion, Programmingdplyr, R4 Comments

A Better Example of the Confused By The Environment Issue

Our interference from then environment issue was a bit subtle. But there are variations that can be a bit more insidious.

Please consider the following.

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September 23, 2018September 26, 2018John MountOpinion, Programming, Tutorialsdplyr, R5 Comments

A Subtle Flaw in Some Popular R NSE Interfaces

It is no great secret: I like value oriented interfaces that preserve referential transparency. It is the side of the public debate I take in R programming.

"One of the most useful properties of expressions is that called by Quine referential transparency. In essence this means that if we wish to find the value of an expression which contains a sub-expression, the only thing we need to know about the sub-expression is its value."

Christopher Strachey, "Fundamental Concepts in Programming Languages", Higher-Order and Symbolic Computation, 13, 1149, 2000, Kluwer Academic Publishers (lecture notes written by Christopher Strachey for the International Summer School in Computer Programming at Copenhagen in August, 1967).

Please read on for discussion of a subtle bug shared by a few popular non-standard evaluation interfaces.

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September 21, 2018September 21, 2018John MountOpinion, Programming, Tutorialsdata.table, dplyr, R, rqdatatableLeave a comment

Timing Column Indexing in R

I’ve ended up (almost accidentally) collecting a number of different solutions to the “use a column to choose values from other columns in R” problem.

Please read on for a brief benchmark comparing these methods/solutions.

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September 20, 2018September 21, 2018John MountCoding, data science, Programming, Tutorialsdata engineering, data wrangling, data.table, dplyr, R15 Comments

Using a Column as a Column Index

We recently saw a great recurring R question: “how do you use one column to choose a different value for each row?” That is: how do you use a column as an index? Please read on for some idiomatic base R, data.table, and dplyr solutions.

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