Mixing R with other languagesJOHN D. COOK, PHD

SINGULAR VALUE CONSULTING

Why R?

Libraries, libraries, libraries

De facto standard for statistical research

Nice language, as far as statistical languages go

“Quirky, flawed, and an enormous success.”

Why mix languages?

Improve performance of R code

Execution speed (e.g. loops)

Memory management

Raid R’s libraries

How to optimize R

Vectorize

Rewrite not using R

A few R quirks

Everything is a vector

Everything can be null or NA

Unit-offset vectors

Zero index legal but strange

Negative indices remove elements

Matrices filled by column by default

$ acts like dot, dot not special

C package interface

Must manage low-level details of R object model and memory

Requires Rtools on Windows

Lots of macros like REALSXP, PROTECT, and UNPROTECT

Use C++ (Rcpp) instead

“I do not recommend using C for writing new high-performance code. Instead write C++ with Rcpp.” – Hadley Wickham

Rcpp

The most widely used extension method for R

Call C, C++, or Fortran from R

Companion project RInside to call R from C++

Extensive support even for advanced C++

Create R packages or inline code

http://rcpp.org

Dirk Eddelbuettel’s book

Simple Rcpp example

library(Rcpp)

cppFunction('int add(int x, int y, int z) { int sum = x + y + z; return sum;}')

add(1, 2, 3)

.NET

RDCOMhttp://sunsite.univie.ac.at/rcom/

F# type provider for R http://bluemountaincapital.github.io/FSharpRProvider/

R.NEThttps://rdotnet.codeplex.com/

SQL Server 2016

execute sp_execute_external_script @language = N'R' , @script = N' OutputDataSet<- data.frame(c("hello"), " ", c("world"));' , @input_data_1 = N' ' WITH RESULT SETS ( ([col1] varchar(20) , [col2] char(1), [col3] varchar(20) ) );

Haskell

HaskellR from Tweag.iohttp://tweag.github.io/HaskellR/

Use quasi-quoting into inline R[r| … |]

Interactive REPL with H wrapper around GHCi

Works with Jupyter notebooks

Emacs org-mode

Crufty but powerful, like all things Emacs

Ships with support for many languages

Works reliably cross-platform

Good for exploration / prototyping

Literate programming

org-babel languages

Supported Other

ABC Dot Ledger Org Screen Axiom Mathematica

Asymptote Ebnf Lilypond Perl Sed Elixir Mathomatic

Awk Elisp Lisp Picolisp Shell Eukleides MongoDB

C Forth Make PlantUML Shen Fomus Neo4j

C++ Fortran Matlab Processing SQL Google translate OZ

Calc Gnuplot Maxima Python SQLite Groovy Prolog

Clojure Haskell Mscgen R Stan HTML Rec

Comint Io OCaml Ruby http request SML

Coq J Octave Sass iPython Stata

CSS Java Scala Julia Tcl

D Javascript Scheme Kotlin Typescript

Ditaa LaTeX LFE

Structure of an org-mode file

Text, images, LaTeX equations, etc.

#+begin_src R…#+end_src

text etc. …

#+begin_src python…#+end_src

Language interop

#+name: sin_r#+begin_src R :var x=0sin(x)#+end_src

#+name: cos_p#+begin_src python :var x=1import mathreturn math.cos(x)#+end_src

#+name: sum_sq#+begin_src perl :var a=3 :var b=4$a*$a + $b*$b#+end_src

#+call: sum_sq(sin_r(1), cos_p(1))

#+results:: 1

Jupyter notebooks

Started out as IPython notebooks

Julia + Python + R

Multiple languages supported (separately)

Less transparent than org-babel

For better: images, formatting, etc.

For worse: Hard to version and diff

Some languages with Jupyter kernels

Bash F# Julia Prolog

C Forth Matlab Python

C++ Go Maxima Ruby

C# Haskell OCaml SAS

Clojure Hy Octave SageMath

Coffeescript J PHP Scala

Common Lisp Java Perl(6) Tcl

Erlang Javascript PowerShell Xonsh

Beaker notebooks

A fork of IPython, predecessor to Jupyter

http://beakernotebook.com/

Cells can be written in different languages

Set attribute on beaker object in one language,access attribute from another language

R data.frame <-> Python pandas.DataFrame

Beaker example

beaker.foo = “Hello world” # Python cell

x <- beaker::get(‘foo’) # R cell

beaker::set(‘answer’, 42) # R cell

z = beaker.answer[0] # Python cell

Languages supportedin Beaker notebooks

C++ Java Python(3)

Clojure JavaScript R

F# Julia Ruby

Groovy Lua/Torch Scala/Spark

HTML Node SQL

R Markdown

Similar to Jupyter, Beaker

http://rmarkdown.rstudio.com

Can mix languages in a single document

Exchange data between languages via data frames

Many publication export formats

Languages supportedin R Markdown

Bash R

CSS Rcpp

JavaScript SQL

Python Stan

R Markdown example

Text (markdown)…

```{r}x <- “hello from R”print(x)```

Text …

```{python}x = “ “.join( [“Hello”, “from”, “Python”] )print(x)```

Summary

Make R more efficient, or borrow its libraries.

R differences: null/NA, vectors, unit offset, etc.

Most of these approaches do not simply install and “just work.”

Org-babel works as documented, but maybe not as expected.

Most general/powerful approach: language <-> Rcpp <-> R

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