An Introduction to R for Policy Analysis: Final Modules Released

The final two modules are now available for ‘An Introduction to R for Policy Analysis’.

Module 5: Data Visualization and Storytelling

Module five is all about applying the principles of effective data visualization and storytelling in R. With a particular focus on using the ggplot2 package, this module introduces some easy-to-follow principles for creating effective plots and data stories in the context of applied policy analysis. Topics covered as part of the module include:

• Why data visualization matters: exploring how data visualization can be useful at both the exploratory and explanatory stages of analysis.
• Rules of thumb for effective data visualization and data storytelling: covering some simple principles to think about when you’re trying to create a plot and craft a narrative around your policy analysis project.
• Building a plot with ggplot2: covering the basic principles of building publication-ready visualizations and plots using the ggplot2 package.
• Working with geometry, labels, colors and themes: providing guidance on how to select the right graph for your analysis and how to use labels, colors and themes to enhance the impact of your plots.
• Factors with the forcats package: factors can be handy when working with categorical data, but are often hard to manage. We’ll cover some functions in the forcats package that can make working with factors easier.
• Stock and flow variables: which provides an overview of the difference between ‘stock’ and ‘flow’ variables and how these concepts can be practically useful in the context of applied policy analysis.
• Spurious correlations: illustrating why many of the relationships encountered in applied policy analysis may be meaningless and how dpyr’s lag() function can be a useful tool for separating spurious and genuine relationships between variables.

Module 6: Reproducible Workflows and Streamlining Analysis

Module six delves into more advanced R programming concepts, with the intention to familiarize learners with a set of simple tools and concepts that are available for creating more robust, repeatable, and efficient policy analysis workflows. As this module covers more advanced programming concepts, it’s core intention is to make newcomers to R aware of what’s possible so they can spot opportunities to apply the topics to their work, rather than making you an expert R programmer. Areas covered include:

• Streamlining and Automating Analysis: providing a practical set of tips for deciding how (and when) to automate your analysis.
• Reproducibility and replicability: suggesting some practical ways for increasing the quality and reproducibility of your analysis.
• An introduction to functions and loops: covering the basics of writing and utilizing functions and loops to help you streamline and automate repetitive aspects of your analysis.
• Unite and separate with tidyr: Techniques to manipulate and clean poorly formatted data.
• Continuing Your R Journey: Providing tips and resources for continuing your learning journey with R.

Further Course Refinements and Housekeeping

I’ll also be spending more time to refine the overall structure and design of course in the coming weeks. I’ll therefore be reaching out to learners to offer my assistance and invite feedback on the course to identify areas of the course that would benefit from further refinement. You’re also welcome to reach out to me directly here if you have any ideas, or are facing any problems with the course.