I’ve visualized the Covid-19 data I’ve got from Pavel Mayer in different ways ( see https://www.rstats-tips.net/2020/08/16/additional-visualization-of-covid-19-development-in-germany/ or https://www.rstats-tips.net/2020/08/09/visualization-of-corona-incidence-in-germany-per-county/ )

But I always wanted to make it more interactive. So I decided to build a Shiny-App.

So what can you do with this app?

Launch the App

Last week I’ve visualized the spread of Covid-19 through Germany using a map-plot.

Now I was asking myself if there’s a better way to show that the rising number of infections these day are other than during the local spreadings in June.

So now I want to plot all of the German Landkreise (similar to counties in the U.S.) regarding the number of new infections during the last 7 days per 100,000 residents using a histogram.

Everyone in Germany is speaking about the 2nd wave of Covid-19. Has it already arrived, is it still coming?

The number of new Covid-19 cases is rising for quite a few weeks According to Johns Hopkins University there were more than 1,000 new cases per day in the last days.

That was also the case when there was only a local hotspot in Gütersloh and Warendorf occurred.

But this time there isn’t one hotspot. The infected people live all over Germany.

So I want to visualize the spreading.

After upgrading dplyr to version 1.0.0 I’m a little bit annoyed by the warnings (actually you can’t suppress them with warnings = FALSE)

1

`summarise()` regrouping output by 'homeworld' (override with `.groups` argument)

But let’s look at an example:

When it comes to programming errors are a natural part of it. Sometimes you want to show such an error and then you want to show how to remove that error.

But if you have an error within your R-code chunk blogdown stops processing your article.