Read R datasets from Python.


Keywords
rdata, r, dataset, conversion, python, python3
License
MIT
Install
pip install rdata==0.6

Documentation

rdata

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Read R datasets from Python.

Installation

rdata is on PyPi and can be installed using pip:

pip install rdata

It is also available for conda using the conda-forge channel:

conda install -c conda-forge rdata

Documentation

The documentation of rdata is in ReadTheDocs.

Simple usage

Read a R dataset

The common way of reading an R dataset is the following one:

>>> import rdata
>>> parsed = rdata.parser.parse_file(rdata.TESTDATA_PATH / "test_vector.rda")
>>> converted = rdata.conversion.convert(parsed)
>>> converted
{'test_vector': array([1., 2., 3.])}

This consists on two steps:

  1. First, the file is parsed using the function parse_file. This provides a literal description of the file contents as a hierarchy of Python objects representing the basic R objects. This step is unambiguous and always the same.
  2. Then, each object must be converted to an appropriate Python object. In this step there are several choices on which Python type is the most appropriate as the conversion for a given R object. Thus, we provide a default convert routine, which tries to select Python objects that preserve most information of the original R object. For custom R classes, it is also possible to specify conversion routines to Python objects.

Convert custom R classes

The basic convert routine only constructs a SimpleConverter objects and calls its convert method. All arguments of convert are directly passed to the SimpleConverter initialization method.

It is possible, although not trivial, to make a custom Converter object to change the way in which the basic R objects are transformed to Python objects. However, a more common situation is that one does not want to change how basic R objects are converted, but instead wants to provide conversions for specific R classes. This can be done by passing a dictionary to the SimpleConverter initialization method, containing as keys the names of R classes and as values, callables that convert a R object of that class to a Python object. By default, the dictionary used is DEFAULT_CLASS_MAP, which can convert commonly used R classes such as data.frame and factor.

As an example, here is how we would implement a conversion routine for the factor class to bytes objects, instead of the default conversion to Pandas Categorical objects:

>>> import rdata
>>> def factor_constructor(obj, attrs):
...     values = [bytes(attrs['levels'][i - 1], 'utf8')
...               if i >= 0 else None for i in obj]
...
...     return values
>>> new_dict = {
...         **rdata.conversion.DEFAULT_CLASS_MAP,
...         "factor": factor_constructor
...         }
>>> parsed = rdata.parser.parse_file(rdata.TESTDATA_PATH
...                                  / "test_dataframe.rda")
>>> converted = rdata.conversion.convert(parsed, new_dict)
>>> converted
{'test_dataframe':   class  value
    1     b'a'      1
    2     b'b'      2
    3     b'b'      3}