Statisticus¶ ↑

Statisticus aims to do three things well:

• Integrate other workflow tools to statistical analysis: memoization, composition, concurrency, and distributed computing

• Remove seams from the R runtime and the R bindings (RSRuby)

• Bridge simple Ruby-centric data containers (hashes, 2-dimensional arrays) into R-centric data containers (lists and data frames)

Usage¶ ↑

The most basic usage can be:

stats_class :geometric_mean

This will:

# create a class, GeometricMean # look for geometric_mean.r somewhere in this gem, in the working directory where the Ruby process is started, or in the ~/.statisticus directory # expect geometric_mean to be a defined function in geometric_mean.r

If that holds true, then you’ve got the easiest access to the R runtime I can think of.

Since I’m using TeguGears here, I have a few other tricks baked in:

• The calls are memoized. So, GeometricMean.call() gets stored in a central repository which can be balanced, pruned, and managed if you’d like.

• The calls can be composed, so things like (SquareRoot | GeometricMean ).call() will work.

• Other features are on their way, just not all tested, like concurrency and distributed processing.

A more interesting example might be:

class Whatever
  # Fill in the gaps here.
end

Decide if I want to move my standard stats libs over here. Possibly all of Sirb as well?

Put all of these into Panorama

Confirm run vs process (from TeguGears)

If you have R code:

If you want to pass the code along:

Dealing with parameter lists

Running this in parallel, memoization

Using DataFrames

Installation¶ ↑

sudo gem install davidrichards-statisticus

Dependencies¶ ↑

• R

• RSRuby

• Log4R

COPYRIGHT¶ ↑

Copyright © 2009 David Richards. See LICENSE for details.