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Category: Fun

R from Python – an rpy2 tutorial

rpy2 Tutorial

Recently I found the Python module rpy2. This module offers a Python interface to R. That is, rpy2 enables us to use the power of R in Python!

Obviously; rpy2 requires that you have both R (version 3.2+) and Python (versions 2.7 and 3.3) installed.  There are pre-compiled binaries available for Linux and Windows (unsupported and unofficial, however). In this short tutorial, I will show you how to do carry out a repeated measures ANOVA (rmANOVA) using the r-packages ‘afex‘ and ‘emmeans‘, Python, and rpy2. The post is now updated and you will find a YouTube video going through the rpy2 examples found in this blog post. You will also find another YouTube Video in which you will learn two methods to show R plots inline in Jupyter Notebooks. Make sure you check them out!

Some good Python Blogs and Resources

In this post you will find some really good Python Blogs and Resources. Most of these really helped me when moving from proprietary software to free and Open Source software (e.g., PsychoPy written in Python).  The links are divided into two categories: general and research. In the ‘general’ category you will find good Python resources that might be more general and introducing. That is, more helpful for general problems and you might want to start here. In the ‘research’ category there are links to good Python resources that are more specific to use in research (e.g., PsychoPy and data analysis). The links in the ‘research’ category mainly contains links to Python blogs.

Collecting data with Scrapy

This rather long (counting the Python code) tutorial assumes that you are interested in scraping data off the web using Scrapy. It might also be limited to Linux.

Scrapy can be installed using pip:

To create a new Scrapy project open up a terminal and create a directory where you will store your Scrapy projects and change do that directory and run this (change ‘psysci’ to whatever your project will be named):

 

Item

We start with the Item class and create an object to store our scraped data in. In my example I scrape the article title, type of article, the articles abstract, when the article was received and when it got accepted for publication, which year it was publicized (and month) and the articles keywords.