Explore the integration of Python and R in this EuroPython 2014 conference talk. Learn how to leverage the powerful statistical capabilities of R within Python projects using pyRserve. Discover the benefits of distributed computing across multiple R servers and see practical code examples demonstrating this powerful setup. Gain insights into R's statistical analysis and graphing functionalities, understand the Rserve and pyRserve connection, and explore topics such as string evaluation, R namespaces, passing functions as arguments, and implementing out-of-bound messages for progress reporting. Enhance your data analysis toolkit by combining the strengths of both Python and R ecosystems.
Overview
Syllabus
Intro
What is R? • A tool for statistical data analysis and graphical representation of data and results • Open Source
What is Rserve?
pyRserve Python-Land
What is pyRserve?
QAP1 Python-Land
QAP1 - Quad Attributes Protocol V1 • Message-orientierted binary protocol Exchange arbitrary complex data
Installation
Easy start...
First real steps: String evaluation Simple expression in R, result returned to Python
More string evaluation • Creating a function in R and calling it
R Namespaces Connector conn provides two special attributes
A more pythonic approach ... (1)
Functions as arguments In R many functions expect functions as arguments
References to variables in R
Out-of-bound messages ... aka callbacks • Allows R to push messages to Python These trigger pre-defined callback functions • Rserve needs to be started with special config file
Basic usage of Out-of-bound messages Setup a callback function in Python
Application of oob (1): progress report
Taught by
EuroPython Conference
