Introduction.

The most recent date in the data is 2021-12-14. Some countries’ most recent data may be somewhat earlier than that date.

New Cases.

@superboreen, Source: https://ourworldindata.org/coronavirus-source-data

Europe.

Europe New Cases.

Europe Cases.

Europe Cases Per Million.

Europe New Deaths.

Africa.

Africa Cases.

Africa Cases/Million.

Africa New Deaths.

Asia.

Asia New Cases.

Asia Cases.

Asia Cases/Million.

Asia New Deaths.

North America.

North America New Cases.

North America Cases.

North America Cases/Million.

North America New Deaths.

South America.

South America New Cases.

South America Cases.

South America Cases/Million.

South America New Deaths.

Oceania.

Oceania Cases.

Oceania Total Cases Per Million.

Oceania New Deaths.

Continents.

Continents New Cases.

Continents Cases.

Continents New Deaths.

About.

Get the disclaimers in first….

I try to refresh this dashboard at least once a day. Sometimes more often.

This is a work in progress. It might change or break at a moment’s notice.

The original motivatation was primarily to use R to try to build an interactive dashboard driven by a set of data downloaded from the European Centre for Disease Control (ECDC).

The first version used the flexdashboard package, which I like a lot and use a lot, but for this purpose it had some issues. The storyboard top navigation wrapped and obscured some of the names of the countries in the plots at the top. So I moved away from flexdashboard and decided to do one scrolling page.

The source of the data is here: https://ourworldindata.org/coronavirus-source-data

The graphs have some issues that I will try to address.

  • I use my own functions to create the plots, passing parameters to vary the dataset, title etc. Using Plotly to convert a static ggplot object to plotly, to make it hover-over interactive, means I lose the subtitle and caption that I pass to my functions. I think it is a known issue. Will keep looking for a fix.

I don’t transform the data I read in. I just subset it and do a little summing. Hopefully any weirdness in the data is because there’s weirdness in the data, rather than because I’ve screwed up something.

N.B. I have noticed a couple of values that look odd in the new deaths data, for example China on 17-04-2020 but I have to assume these are keying errors that may get fixed in the dataset over time.

Any helpful comments can be sent to superboreen at gmail dot com.