Well, when we can easily embed SQL code within either R or Python script, why not blend R and Python together?There are basically two approaches by which we can use both Python and R side by side in a single project.
R within PythonPypeRPypeR provides a simple way to access R from Python through pipes.
PypeR is also included in Python’s Package Index which provides a more convenient way for installation.
PypeR is especially useful when there is no need for frequent interactive data transfers between Python and R.
By running R through pipe, the Python program gains flexibility in sub-process controls, memory control, and portability across popular operating system platforms, including Windows, GNU Linux and Mac OSConventions for conversion of Python objects to R objectspyRservepyRserve uses Rserve as an RPC connection gateway.
Through such a connection, variables can be set in R from Python, and also R-functions can be called remotely.
R objects are exposed as instances of Python-implemented classes, with R functions as bound methods to those objects in a number of cases.
rpy2rpy2 runs embedded R in a Python process.
It creates a framework that can translate Python objects into R objects, pass them into R functions, and convert R output back into Python objects.
rpy2 is used more often since it is one which is being actively developed.
One advantage of using R within Python is that we would able to use R’s awesome packages like ggplot2, tidyr, dplyr et al easily in Python.
As an example let’s see how we can easily use ggplot2 for mapping in Python.
html#geometryResourcesYou may want to have a look at the following resources for more in-depth review of rpy2:rpy2’s Official DocumentationRPy2: Combining the Power of R + Python for Data ScienceAccessing R from Python using RPy2Python with RWe can run R scripts in Python by using one of the alternatives below:rJythonThis package implements an interface to Python via Jython.
It is intended for other packages to be able to embed python code along with R.
rPythonrPython is again a Package Allowing R to Call Python.
It makes it possible to run Python code, make function calls, assign and retrieve variables, etc.
SnakeCharmRSnakeCharmR is a modern overhauled version of rPython.
It is a fork from ‘rPython’ which uses ‘jsonlite’ and has a lot of improvements over rPython.
PythonInRPythonInR makes accessing Python from within R very easy by providing functions to interact with Python from within R.
reticulateThe reticulate package provides a comprehensive set of tools for interoperability between Python and R.
Out of all the above alternatives, this one is the most widely used, more so because it is being aggressively developed by Rstudio.
Reticulate embeds a Python session within the R session, enabling seamless, high-performance interoperability.
The package enables you to reticulate Python code into R, creating a new breed of a project that weaves together the two languages.
The reticulate package provides the following facilities:Calling Python from R in a variety of ways including R Markdown, sourcing Python scripts, importing Python modules, and using Python interactively within an R session.
Translation between R and Python objects (for example, between R and Pandas data frames, or between R matrices and NumPy arrays).
Flexible binding to different versions of Python including virtual environments and Conda environments.
ResourcesSome great resources on using the reticulate package are:The Documentation is pretty robust and has a lot of examples and use cases to help you get started.
com/2017/04/10/test-driving-python-integration-in-r-using-the-reticulate-package/SNAKES IN A PACKAGE: COMBINING PYTHON AND R WITH RETICULATEConclusionBoth R and Python are quite robust languages and either one of them is actually sufficient to carry on the Data Analysis task.
However, there are definitely some high and low points for both of them and if we could utilize the strengths of both, we could end up doing a much better job.
Either way, having knowledge of both will make us more flexible thereby increasing our chances of being able to work in different environments.
References:Interfacing R and Python — Andrew Collierhttp://blog.