computingJulia

Julia + R for Data Science

Stefan Karpinski

u alj i

Center For Data Science

In a single project, I was using:

‣ Matlab for linear algebra

‣ R for stats & visualization

‣ C for the fast stuff

‣ Ruby to tie it all together

My Data Science Stack circa 2009

What is the “Two Language Problem”?

a.k.a. “Ousterholt’s dichotomy”

“systems languages” “scripting languages”static dynamic

compiled interpreteduser types standard types

fast slowhard easy

What is the “Two Language Problem”?

Because of this dichotomy, a two-tier compromise is standard:

‣ for convenience, use a scripting language (Python, R, Matlab)

‣ but do all the hard stuff in a systems language (C, C++, Fortran)

Pragmatic for many applications, but has drawbacks

‣ aren’t the hard parts exactly where you need an easier language?

‣ forces vectorization everywhere, even when awkward or wasteful

‣ creates a social barrier – a wall between users and developers

The Julian Unification

Ousterholt’s dichotomy

static dynamic

compiled interpreted

user types standard types

hard easy

fast slow

The Julian Unification

dynamic

compiled

user types standard types

Julia

and

easy

fast

Speedtim

e re

lativ

e to

C

Speed vs. Productivitytim

e re

lativ

e to

C

normalized lines of code

Julia

Rube Goldberg Revised

Here’s my data science stack today:

‣ Matlab Julia for linear algebra

‣ R Julia for stats & visualization

‣ C Julia for the fast stuff

‣ Ruby Julia to tie it all together

Why Try Julia?

‣ Because it’s fun!

some people enjoy trying out and learning new languages

‣ You’re in a world of pain with other tools

R / Python / Matlab isn’t fast enough for the work you doRcpp / Cython / C++ are unappealing, not productive enoughJulia is in the sweet spot for speed & high productivity

‣ For the fancy features

multiple dispatch, coroutine-based I/O, macros & metaprogramming,efficient custom types, advanced linear algebra, …