holliPista
2021 03 16
There are no more current topic than the Covid-19. As the ‘Shiny Application and Reproducible Pitch’ I’ve made a quick visualization of the Hungarian fatality and share of positive tests. It’s the seed of a prediction project but just the first explanatory step, hence please do not expect any fancy thing.
You can find the project on the following github link: https://github.com/hollipista/CovidHunShinyPred
Further information about the data: https://atlo.team/koronamonitor/
And here is the shiny app: https://hollipista.shinyapps.io/CovidHunShinyPred/
I have a subsection of original dataset for further predictions, you can see below:
## # A tibble: 6 x 9
## Date NewCases ActiveInfected PositiveTestSHR Fatality Recovered
## <date> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 2020-03-04 2 2 <NA> NA NA
## 2 2020-03-05 2 4 <NA> NA NA
## 3 2020-03-06 0 4 <NA> NA NA
## 4 2020-03-07 1 5 2,00% NA NA
## 5 2020-03-08 2 7 4,65% NA NA
## 6 2020-03-09 2 9 <NA> NA NA
## # ... with 3 more variables: Hospitalized <dbl>, VentilatorTreated <dbl>,
## # Vaccinated <dbl>
As you can see there’s quite huge fluctuation in the figures. We have to handle the variability of the variables. It has a noisy nature of course but the uneven data provision is also an important factor.
I’ve chosen the share of positive test variable for this project. You can see a linechart made by ggplot, with the absolute number of daily fatal cases and the mentioned positive test share data.
You can apply different moving averages stepped by days (default is 0 that means no applied moving average), and also shift the tests with lag function as the identified amount of diseased results in lethal results with a certain delay - of course.
Please do not take my project seriously, it’s about a tiny play around shiny.
And again, here is a the app: https://hollipista.shinyapps.io/CovidHunShinyPred/
Thanks for you grading!