Accessing R from Python using RPy2

This past Tuesday I had the opportunity to present a short talk (a bit long) related to text mining at the Los Angeles R Users’ Group. Since I do most of my text mining in Python, I took this opportunity to discuss RPy2, an interface to R from Python. My slides are below:

Accessing R from Python using RPy2
View more presentations from Ryan Rosario.

Download/view slides here. Topics include

• Using Python with R with an example using web mining.
• Web mining using pure R rather than Python.

Code for demonstration is here:

1. offtopic_demo.py is a pure Python script that extracts data from a web forum and dumps it to disk. To actually use it, you will need to register for an account.
2. RPy2_demo.py reads the data from the forum from disk and calls R from Python to perform some basic analysis.
3. curljson_demo.R grabs some JSON data from the Twitter Search API using RCurl and converts it to R lists using rjson.

Video:

Running the code requires some packages that you need to install.

• twill package for web browsing, that installs a Python package for you. Requires the mechanize package as well. twill is a wrapper to mechanize.
• BeautifulSoup package for Python for HTML parsing.
• R must be built to use as a shared library using --enable-R-shlib, otherwise Python cannot call it.
• RPy2, the Python interface to R.

To see the main talk of the evening, click here.

Some Recommended Books

Natural Language Processing

• Foundations of Statistical Natural Language Processing, Manning and Schuetze.
• Speech and Language Processing, Jurafsky and Martin.
• Natural Language Processing and Text Mining, Kao and Poteet.

Text Mining

• Practical Text Mining with Perl, Bilisoly. See my review of this book in the Journal of Statistical Software here which is also excerpted on Amazon!
• Text Mining: Applications and Theory, Berry and Kogan (NEW).
• The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data, Feldman and Sanger.
• Mastering Regular Expressions, Friedl.

Data Mining

• Elements of Statistical Learning: Data Mining, Inference and Prediction. Hastie, Tibshirani and Friedman.
• Data Mining: Concepts and Techniques (recommended by @nealrichter). Han, Kamber and Pei. 
• Data Mining: Practical Machine Learning Tools and Techniques [the fern book]. Witten and Frank.
• Introduction to Data Mining [the rock book]. Tan, Steinbach, Kumar.

Web Mining

• Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, Liu.
• Mining the Web: Discovering Knowledge from Hypertext Data, Chakrabarti.
• Mining Graph Data, Cook and Holder.
• Managing and Mining Graph Data, Aggarwal and Wang.
• Social Network Analysis: Methods and Applications, Wasserman and Faust.