pyRserve is a library for connecting Python to R <http://www.r-project.org/>
(an excellent statistic package). Running Rserve <http://www.rforge.net/Rserve/>
in R attaches the R-interpreter to a network socket, waiting for pyRserve to connect to it.
Through such a connection, variables can be get and set in R from Python,
and also R-functions can be called remotely.
In contrast to rpy or rpy2 <https://rpy2.github.io/>_ the R process does not have to
run on the same machine, it can run on a remote machine and all variable access and
function calls will be delegated there through the network.
Furthermore - and this makes everything feel very pythonic - all data structures will automatically be converted from native R to native Python and numpy types and back.
This package has been mainly developed under Linux, and hence should run on all standard unix platforms, as well as on MacOS. pyRserve has also been successfully used on Windows machines. Unittests have been used on the Linux and MacOS side, however they might just work fine for Windows.
It has been tested to work with Python 2.7.x, 3.6 to 3.9.
The latest development has been tested with some previous and current versions of R and Rserve.
pyRserve has been written by Ralph Heinkel (ralph-heinkel.com) <https://ralph-heinkel.com/> and is
released under MIT license <https://github.com/ralhei/pyRserve/blob/master/LICENSE>.
From your unix/macOS,windows command line run::
pip install pyRserveFor a fully functional setup also R and Rserve have to be installed. See section
installation <https://pyrserve.readthedocs.io/en/latest/installation.html>_ in the pyRserve
documentation for instructions.
Open a first shell and start up the R server, by calling the module Rserve that provides
the actual network connectivity for R::
$ R CMD RserveR (Rserve) will now listen on port 6311 (on localhost). Of course Rserve can be configured to listen on an exposed port and hence will be accessible from remote hosts as well.
Open a second shell, start Python, import pyRserve, and initialize the connection to Rserve::
$ python
>>> import pyRserve
>>> conn = pyRserve.connect()The default connection will be done on localhost:6311. Other hosts can be reached by
calling pyRserve.connect(host=..., port=...) as well.
The conn object provides a namespace called conn.r that directly maps all variables
and other global symbols (like functions etc) and hence makes them accessible from Python.
Now create a vector in R, access the vector from Python (will be converted into a numpy array), and
call the sum()-function in R::
>>> conn.r("vec <- c(1, 2, 4)")
>>> conn.r.vec # access vector 'vec' as an attribute of 'conn.r'
array([1., 2., 4.])
>>> conn.r.sum(conn.r.vec) # 'sum' in running in the R-interpreter, returning the result to Python
7.0The other way around also works::
>>> conn.r.somenumber = 444 # set a variable called 'somenumber' in the R interpreter...
>>> conn.r("somenumber * 2") # ... and double the number
888.0pyRserve is now hosted on GitHub at <https://github.com/ralhei/pyRserve>_.
Documentation can be found at <https://pyrserve.readthedocs.io>_.
For discussion of pyRserve and getting help please use the Google newsgroup
available at <http://groups.google.com/group/pyrserve>_.
Issues with the code (like bugs, etc.) should be reported on GitHub at
<https://github.com/ralhei/pyRserve/issues>_.