Conversion of DataFrame to R's data.frame
Although I have already produced code for this (see below) I'm posting this as an issue rather than a pull request to discuss the design, because there are some issues open in my code:
- Series of dtype object need an explicit cast or rpy2's numpy conversion will treat them improperly
- The performance has not been profiled
- Probably some room for optimizations
- Proper name for the function
- The generation of an intermediate OrdDict object may cause problems in case of very large datasets

The code in the current form is posted below. If there is interest, I will work towards integrating it in pandas.rpy.common and add unit tests.

``` python
import numpy as np
import rpy2.robjects as robjects
import rpy2.robjects.numpy2ri as numpy2ri
from rpy2.robjects.packages import importr
import rpy2.rlike.container as rlc

def dataset_to_data_frame(dataset, strings_as_factors=True):

    # Activate conversion for numpy objects
    robjects.conversion.py2ri = numpy2ri.numpy2ri
    robjects.numpy2ri.activate()

    base = importr("base")
    columns = rlc.OrdDict()

    for column in dataset:
        value = dataset[column]

        # object type requires explicit cast
        if value.dtype == np.object:
            value = robjects.StrVector(value)
             #FIXME: how to generalize it?
            if not strings_as_factors:
                value = base.I(value)

        columns[column] = value

    dataframe = robjects.DataFrame(columns)
    dataframe.rownames = robjects.StrVector(dataset.index)

    # To prevent side-effects in other code
    robjects.conversion.pi2ri = robjects.default_py2ri

    return dataframe
```
