Everybody seems to be starring at plots outlining the spread of the pandemic these days. One thing that caught my interest is how relative small differences in design choices can influence the message that a visual displays. Using the Financial Times Plots graphs developed by John Burn-Murdoch as an example, I explore the visualizer degrees of freedom.

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Assessing the impact of Non-Pharmaceutical Interventions on the spread of Covid-19 requires data on Governmental measures. Luckily, the Assessment Capacities Project (ACAPS) and the Oxford Covid-19 Government Response Tracker both provide such data. In this blog post, I explore the new data provided by the Oxford initiative and compare it against the data provided by ACAPS that is already included in my {tidycovid19} package.

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I have decided that the world needs another Covid-19 related R package. Not sure whether you agree, but the new package facilitates the direct download of various Covid-19 related data (including data on governmental measures) directly from authoritative sources. It also provides a flexible function and accompanying shiny app to visualize the spreading of the virus. Play around with the shiny app here if you like or hang around to learn more about the package.

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Following up on prior posts I now document on how to obtain and merge data on government measures (like social distancing measures or lockdown rules) provided by the Assessment Capacities Project (ACAPS) with the Covid-19 data from the Johns Hopkins CSSE team. In the longer run, this merged data can be used to study the impact of interventions on the global spread of the virus.

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Assume that you have some new data that you want to explore. The new CRAN version of the ‘ExPanDaR’ package helps by providing a (customized) R notebook containing all building blocks of an exploratory data analysis with a few clicks. Let me show you how to do this by taking a quick look at country-level CO2 emissions.

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Closing down for the year, I finally wrapped up a new ‘ExPanDaR’ version that now allows exploring all sorts of data interactively and generates notebooks containing the analysis on the fly. So here comes my little Christmas present for the wonderful RStats community!

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Online appendices detailing the robustness of empirical analyses are paramount but they never let readers explore all reasonable researcher degrees of freedom. Simonsohn, Simmons and Nelson suggest a ‘specification curve’ that allows readers to eyeball how the main coefficient of interest varies across a wide arrange of specifications. I build on this idea by making it interactive: A shiny-based web app enables readers to explore the robustness of findings in detail along the whole curve.

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