Friday, October 25, 2013

Thursday, October 17, 2013

Julia - analysis language, compared to R (psst: Python looks pretty good compared w/ R)

You might enjoy these links:

Efficiency and terseness, compared to R:

An example, in a blog post:

The source, and home page:

Take special note of Julia vs. Python vs. R performance here (I am surprised at Python vs. R):

(note: there is an Ipython notebook backend for Julia)

and of an overview comparison with R:

John Myles White's talk about streaming data analysis, and managing memory:

Finally, here's a comparison of some data analysis tools:
  • Matlab (really Octave);
  • R
  • Julia
  • it seems they almost got to Python (just a mention)

Tuesday, October 15, 2013

Does this sound familiar? (a reality from any data, software or almost any other activity)

Apologies to MK - I was posting posts, and saw this draft from her, from earlier in the year (January).

It pointed to a good read - one that hit a particularly vocal chord.

In case she she rescinded her intent to share, then I am solely responsible.

A good meta-parable, a good read:

ggplot for python & random forrest regression in

If you already combine R and Python in IPython-notebooks, then you probably already use R-magic (calling R from ipython).

In that case, you may appreciate a port of ggplot2 to Python:

The immediately prior post compares performance and results of random forrest regression in R and Python:

R and STATA in Statistical analysis of politics

Perhaps the proliferation of big data is leading the charge to analyze.

The question of any analysis lies in - what does your model say, and how valid is it?

Inspect and run it yourself is always a bottom line answer.

Here's one such (note sources for both R and STATA, if you browse the site):

This via a recent reference by Joshua Holland on -

I had imagined a site like this, only with some correlation to the purpose and benefactors of any particular bill (which would be easier if they were single topic items, of course). In any case, for all you "data" fans, here it is.

There are a few parts of this which I find interesting:

  • The Polarization of the Parties

The polarization diagram is best viewed in the context of this page, but since there are no tags to internally link to the various sections, I pulled out the image separately as a way to point to it.

Be sure to play with your own analysis - R-Studio ( or IPython Notebooks, with the "R magic" extension should do it ( and should help show the way).

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