A Julia Meta Tutorial

July 11, 2013

(This article was first published on Data Community DC » R, and kindly contributed to R-bloggers)


If you are thinking about taking Julia, the hot new mathematical, statistical, and data-oriented programming language, for a test drive, you might need a little bit of help. In this blog we round up some great posts discussing various aspects of Julia to get you up and running faster.

Why We Created Julia

If only you could always read through the intentions and thoughts of the creators of a language! With Julia you can. Jump over to here to get the perspectives of four of the original developers, Jeff BezansonStefan KarpinskiViral Shah, and Alan Edelman.

We are power Matlab users. Some of us are Lisp hackers. Some are Pythonistas, others Rubyists, still others Perl hackers. There are those of us who used Mathematica before we could grow facial hair. There are those who still can’t grow facial hair. We’ve generated more R plots than any sane person should. C is our desert island programming language.

We love all of these languages; they are wonderful and powerful. For the work we do — scientific computing, machine learning, data mining, large-scale linear algebra, distributed and parallel computing — each one is perfect for some aspects of the work and terrible for others. Each one is a trade-off.

We are greedy: we want more.

An IDE for Julia

If you are looking for an IDE for Julia, check out the Julia Studio. Even better, Forio, the makers of this IDE, offer a nice series of beginner, intermediate, and advanced tutorials to help you get up and running.

 Julia Documentation

By far the most comprehensive and best source of help and information on Julia are the ever growing Julia Docs which includes a Manual for the language (with a useful getting started guide), details of the Standard Library, and an overview of available packages.  Not to be missed are the two sections detailing noteworthy differences between Matlab and R.

MATLAB, R, and Julia: Languages for data analysis

Avi Bryant provides a very nice overview and comparison of Matlab, R, Julia, and Python. Definitely recommended reading if you are considering a new data analysis language.

An R Programmer Looks at Julia

This post is from mid-2012 so a lot has changed with Julia. However, it is an extensive look at the language from an experienced R developer.

There are many aspects of Julia that are quite intriguing to an R programmer. I am interested in programming languages for “Computing with Data”, in John Chambers’ term, or “Technical Computing”, as the authors of Julia classify it. I believe that learning a programming language is somewhat like learning a natural language in that you need to live with it and use it for a while before you feel comfortable with it and with the culture surrounding it. Read more …

The State of Statistics in Julia – Late 2012 

Continuing on this theme of statistics and Julia, John Myles White provides a great view of using Julia for statistics which he updated in December of last year.

A Matlab Programmer’s Take on Julia – Mid 2012

A quick look at Julia from the perspective of a Matlab programmer and pretty insightful as well.

Julia is a new language for numerical computing. It is fast (comparable to C), its syntax is easy to pick up if you already know Matlab, supports parallelism and distributed computing, has a neat and powerful typing system, can call C and Fortran code, and includes a pretty web interface. It also has excellent online documentation. Crucially, and contrary to SciPy, it indexes from 1 instead of 0.  Read more …

Why I am Not on the Julia Bandwagon Yet

Finally, we leave you, good reader, with a contrarian view point.

The post A Julia Meta Tutorial appeared first on Data Community DC.

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