A recent update to the {tidycovid19} package brings data on testing, alternative case data, some regional data and proper data documentation. Using all this, you can use the package to explore the associations of (the lifting of) governmental measures, citizen behavior and the Covid-19 spread.

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Yesterday, I came across the Google “COVID-19 Community Mobility Reports“. In these reports, Google provides some statistics about changes in mobility patterns across geographic regions and time. The data seem to be very interesting to assess the extent of how much governmental interventions and social incentives have affected our day-to-day behavior around the pandemic. Unfortunately, the data comes in by-country PDFs. What is even worse, the daily data is only included as line graphs in these PDFs. Well, who does not like a challenge if it is for the benefits of open science?

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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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Author's picture

Joachim Gassen

Curious researcher, passionate teacher and coding nerd.

Professor of accounting at Humboldt-Universität zu Berlin

Berlin