

A Comparison of Programming Languages (in Economics) - jeffreyrogers
http://jonathankinlay.com/index.php/2015/02/comparison-programming-languages/

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jeffreyrogers
Here is the paper the conclusions are coming from:
[http://economics.sas.upenn.edu/~jesusfv/comparison_languages...](http://economics.sas.upenn.edu/~jesusfv/comparison_languages.pdf).
I linked to this blog post instead because it gives a better summary of the
paper for people who don't want to read the whole thing.

The author of the blog post raised a few points that I thought were
interesting and that I'll provide my own answers to here. Feel free to comment
if you disagree with me.

> Why would anyone prefer Python, given that there is a much faster, free
> alternative in Julia, which is just as easy a language to program in?

I've used Julia a bit and the lack of libraries was frustrating. It is getting
better now, but you'll still have to write a lot of stuff from scratch if you
decide to use it.

> What justification is there for preferring R to Matlab, other than cost?

R makes it very easy to perform exploratory analysis on a dataset. Matlab
might be similar, but I don't have experience with it.

> Why does anyone bother with Java? If speed is the critical issue, there are
> faster alternatives. If you like the relative simplicity of the syntax,
> Julia is cleaner, simpler and just as fast in execution.

I agree with this. I've never enjoyed programming in Java, but some people
seem to like the large number of libraries available.

------
lygi
> What justification is there for preferring R to Matlab, other than cost?

My impression is that the primary problem domains for these two are pretty
different.

Matlab is pretty heavily targeted at folks doing numerical analysis [1], e.g.
solving differential equations related to physics/engineering/economics, and
has its origins as a wrapper around existing fortran implementations for
numerical linear algebra. Engineers, especially, use Matlab all over the
place.

The R project explicitly is interested with statistics [2], and as someone
earlier pointed out, make is pretty easy to do analysis on a dataset. Though,
I have pretty limited experience with R, so I could be mistaken.

[1] Cleve Moler. Numerical Computing with MATLAB.
[http://www.mathworks.com/moler/chapters.html](http://www.mathworks.com/moler/chapters.html)
This is the guy who wrote the first Matlab implementations, and founded
Mathworks.

[2] The R Project for Statistical Computing.
[http://www.r-project.org/](http://www.r-project.org/)

------
pjz
> Why would anyone prefer Python, given that there is a much faster, free
> alternative in Julia, which is just as easy a language to program in?

Python also has a much more mature ecosystem (read: more pre-written libraries
to do what you want to do), and delivers some of the UI benefits of
Mathematica if you take into account ipython notebooks and packages like
Bokeh.

~~~
todd8
I'm really looking forward to Julia, I like the language and its performance,
but this blog post by Dan Luu is discouraging:
[http://danluu.com/julialang/](http://danluu.com/julialang/).

------
lscore720
Thanks for sharing! As a non-technical guy in the industry, love this summary
of useful research that was well over my head.

