Data visualization

Agency proliferation: processing the dataset of institutional characteristics of regulatory agencies - RegGov 2018

Introduction This tutorial shows how to process the dataset of institutional characteristics of regulatory agencies in R. It presents the dataset presented at the journal article entitled “Agency proliferation and the globalization of the regulatory state: Introducing a data set on the institutional features of regulatory agencies”, by Jacint Jordana, Xavier Fernández-i-Marín and Andrea C. Bianculli, published at Regulation & Governance (December 2018). The necessary packages in R to follow the instructions are the following:

Rule growth and government effectiveness: why it takes the capacity to learn and coordinate to constrain rule growth

This paper asks whether strong bureaucracies can effectively constrain the continuously growing stock of rules in modern democracies through organizational coordination and learning. To answer this question, the paper analyzes the growth of rule …

Using ggmcmc

There is a pdf version of Using the ggmcmc package. Why ggmcmc? ggplot2, based on the grammar of graphics (Wilkinson et al. 2005), empowers R users by allowing them to flexibly crate graphics (Wickham 2009). Based on this idea, ggmcmc is aimed at bringing the design and implementation of ggplot2 to MCMC diagnostics, allowing Bayesian inference users to have better and more flexible visual diagnostic tools. ggplot2 is based on the idea that the input of any graphic is a data frame mapped to aesthetic attributes (colour, size) of geometric objects (points, lines).