R dataset
Motivation for this pull request came from this problem: http://stackoverflow.com/q/19039356/190597.

The problems:
- `pandas.rpy.common.load_data`  currently fails to load high-dimensional arrays such as the "Titanic" dataset. `load_data` prints an error to stdout and returns None.
- Applying `load_data` to "iris3", "state.division" or "state.region" R datasets currently raise a TypeError.
- Timeseries R data is currently converted to lists that lack a time series index.
- Factor data is  converted to a list of index numbers without any levels information.

This pull request:
- Provides code which allows `load_data` to convert the "Titanic" dataset (and similar high-dimensional arrays) to "melted" DataFrames. The code _does not_ use R's melt function, since that would introduce a new dependency.
- load_data now returns a DataFrame for arrays like "iris3".
- load_data converts R factors like "state.division" and "state.region" to lists which use the levels as values.
- load_data converts ts (timeseries) R data, like "airmiles" to Series with an index showing the times. Note however I wasn't able to convert the index to a DatetimeIndex since the same code is used to convert numeric R data (like "euro") which does not have a time-related index.
- moves already existant tests from rpy/common.py to tests/test_rpy.py
- adds new unit tests which highlight the problems mentioned above, and tests for what I think is more desireable behavior as described above.
