Blog Posts about Covid19 that Use R

My goal is to help those who want to analyze Covid-related data and want to do so by unleashing the capabilities of R and its community.

In early March, as the Covid-19 pandemic spread throughout the news, I noticed two or three posts on blocks that used R to analyze data or explain the spread of infection. Over the next week or so, I collected more posts from R-Bloggers, Twitter, R-Weekly and other sources; by the end of March the collection numbered 26.

Then the excellent post of Antoine Soetewey https://www.statsandr.com/blog/top-r-resources-on-covid-19-coronavirus/ came to my attention and I emailed its author. For the past two months I have scoured as many potential sources as I could of such posts, organized them, and sent them on to Antoine.

In early June, I began collecting data about the people who had posted. Where background information was available, I captured their role (occupation), the country where they live, and how to reach them by email or Twitter.

This post presents descriptive data from the 119 posts collected as of June 13th. The post focuses on the dates posts were published, the roles of the bloggers, and their country. Later posts will comment on the data sources cited in the posts, the R packages used, and the statistical or mathematical techniques applied to the data. After that, my hope is to create a concept network graph and explore the relationships between posts. Meanwhile, the collecting will proceed apace.

If anyone has corrections to the data set or knows other blog posts that ought to be included in it, please write me at Rees (at) ReesMorrison (dot) com.

The data set analyzed in this post is available on GitHub https://github.com/ReesMorrison/Covid-Blog-Posts in the file “Blog Summary”.

The first graphic displays the pace of posts from the first one that I have found (the fifth week of 2020, early February).

Bloggers, other than professors, describe their work-day roles in a variety of ways. I tried to standardize those various terms and description, but it is quite possible that I have misrepresented what some of these bloggers do for a living. For that I apologize, and would welcome corrections. Going further, I assigned the bloggers into broader role categories. Those broader categories are represented as columns in the following chart, with the segments showing the more granular roles I assigned.

The listing does not include co-authors, of which the set has at least three. D. Ardia with Emanuele Guidotti, Louisa Jorm with Tim Churches and Jonathan Dushoff with Ben Bolker.

The labels indicate the number of posts in the data set for each role within a role category.

Finally, here is a (skimpy) choropleth of the countries of the bloggers. The number of posts from the USA and Australia are 28 and 20, respectively. Germany (16) and and Canada (15) follow.

An enthusiast of the four genre who likes to write (and use R software)