Add an experimental CompositeDataFrames type.

This is an AbstractDataFrame built using Composite types. The advantages
of this are:

* Accessing columns `df[:colA]` is more type stable, so code should be
  faster (without `@with` tricks). There is still the function boundary
  to worry about.

* You can access single columns directly using df.colA.

* All indexing operations can be done currently.

Some drawbacks include:

* As an abuse of the type system, creating a new type for each change to
  a DataFrame may waste memory.

* You cannot change the structure of a CompositeDataFrame once created.
  You have to treat it (almost) like an immutable object. For example to
  add a column, you need to do something like:

    transform(df, newcol = df.colA + 5)

  An advantage of this is that the API becomes more functional. All
  manipulations of the CompositeDataFrame return a new object. Normally,
  this doesn't create much more memory.

If nothing else, this is a good test of DataFrames supporting more
AbstractDataFrame types.
Updated the README for CompositeDataFrames.
Merge pull request #7 from JuliaStats/compositedataframe

Add an experimental CompositeDataFrames type.