My relationship with reading borders on pathological (and by “borders on” I mean “has literally been a topic of discussion in therapy”). I mean, I’ve gotten it under control somewhat —we’ll use my 2014 Goodreads Reading Challenge as a bar for a bit out of control—which means I can take a look back on my 2021 year in books without too much self-recrimination.

Read the previous post first! This post is a sequel to the previous one on Bayesian propensity scores and won’t make a lot of sense without reading that one first. Read that one first! In my previous post about how to create Bayesian propensity scores and how to legally use them in a second stage outcome model, I ended up using frequentist models for the outcome stage.

This post combines two of my long-standing interests: causal inference and Bayesian statistics. I’ve been teaching a course on program evaluation and causal inference for a couple years now and it has become one of my favorite classes ever.

In most of my research, I work with country-level panel data where each row is a country in a specific year (Afghanistan in 2010, Afghanistan in 2011, and so on), also known as time-series cross-sectional (TSCS) data.

You’ve experienced an HTTP redirect (or URL redirect, or URL forwarding) even if you haven’t noticed. We all use browsers (I assume, since you are reading this), either on a phone or laptop/desktop computer. Browsers don’t show all the HTTP requests going on in the background, some of which are redirects.

This final installment in our series on using the Highcharts accessibility module with {highcharter} is basically “extra credit.” (Read: I took the time to figure this out, so I’m sharing it. But, it’s probably not the most helpful post if you’re just trying to get the accessibility module working with {highcharter}.) We covered enabling the accessibility module, and getting basic keyboard navigation in part one.

Back in the first post in this series I mentioned that the impetus for this whole endeavor was Silvia Canelón’s excellent collection of R-specific resources for making data visualizations more accessible (Canelón 2021). I wanted to fill the gap between the possibilities afforded by Highcharts’ accessibility module, and documented examples of module use with the {highcharter} package (Kunst 2021). Don’t worry, I have pull requests in the works

In the first two parts of this series we introduced the Highcharts accessibility module, the {highcharter} R package, and created some working examples with accessibility features turned on. Here, we’ll follow in the vein as part 2 by re-creating one of the accessible chart demos from Highcharts.

Today we’ll be using the Joshua Kunst’s {highcharter} package to re-create the first example from the Highcharts documentation for its accessibility module, an accessible line chart showing screen reader popularity over time, as an htmlwidget.

At the end of my previous post on beta and zero-inflated-beta regression, I included an example of a multilevel model that predicted the proportion of women members of parliament based on whether a country implements gender-based quotas for their legislatures, along with a few different control variables. I also included random effects for year and region in order to capture time- and geography-specific trends.