You should probably use ‘joy plots’ to visualize varying effects’ posteriors, because they look great.

In this tutorial, I’ll show how to use R to quantitatively explore, analyze, and visualize a research literature, using Psychonomic Society publications. This post directly continues from part I of Quantitative literature review with R. Please read that first for context. Part I focused on data cleaning and simple figures, but here we will look at relational data by visualizing some network structures in the data.

In this tutorial, I’ll show how to use R to quantitatively explore, analyze, and visualize a research literature, using Psychonomic Society’s publications

Today I’ll share some tips on elementary web scraping with R. Our goal is to download and process an entire Wordpress blog into an R data frame, which can then be visualized and analyzed for fun and discovery. We’ll scrape andrewgelman.com, the home of “Statistical Modeling, Causal Inference, and Social Science”. This is a very popular statistics and social science blog, whose main author, Andrew Gelman is a famous statistician and political scientist, and author of such classic holiday thrillers as Bayesian Data Analysis and Data Analysis Using Regression and Multilevel Models.

I have a little python script on my work computer that tracks hours I’ve spent in the office (assuming that those hours begin with a computer login, and end in a computer shutdown–sadly this is almost always true.) I don’t really do anything with the information, and it is really a remnant of my time as a contract worker. However, for shared or billed projects, such automation of data gathering can be very valuable.