covidrecon

R tools for monitoring effectiveness of COVID-19 control efforts

See also https://github.com/CBDRH/ozcoviz

Installation

The development version from GitHub with:

# install.packages("devtools")
devtools::install_github("CBDRH/covidrecon")

Why Another COVID19 R package?

We created an open source dashboard to combine value-adding data visualisations with updated statistical analysis not yet widely seen. We decided to package up a lot of the code used in the dashboard into this R package.

Now, there are many other R packages for pulling COVID19 data, so why create another one? A few reasons. The main one being that the COVID9 data formats, and other other R packages are changing rapidly. We wanted to perform statistical analysis that aren’t widely available. We needed to ensure the data stayed in a format that was easily usable for our purposes. We needed something that we could control and add features to ourselves, that wouldn’t need to rely on from another person.

This package is still changing and evolving, and it’s primary purpose is to implement code

Usage

covidrecon is designed to add statistical analysis and data visualisations of covid19 data. It was created to power this dashboard.

Pulling data

The data source that we recommend using is from the European CDC. You can get this data with covid_latest(). This will by default cache the data downloaded for that day. Here is what the data looks like.

library(covidrecon)
covid <- covid_latest()
#> New names:
#> * dateRep -> date_rep
#> * countriesAndTerritories -> countries_and_territories
#> * geoId -> geo_id
#> * countryterritoryCode -> countryterritory_code
#> * popData2018 -> pop_data2018
#> New names:
#> * dateRep -> date_rep
#> * countriesAndTerritories -> countries_and_territories
#> * geoId -> geo_id
#> * countryterritoryCode -> countryterritory_code
#> * popData2018 -> pop_data2018
#> covid data extracted from 2019-12-31 UTC to 2020-04-14 UTC
covid
#> # A tibble: 10,742 x 13
#>    date                country_region deaths cases cumulative_cases
#>    <dttm>              <chr>           <dbl> <dbl>            <dbl>
#>  1 2020-03-03 00:00:00 Andorra             0     1                1
#>  2 2020-03-14 00:00:00 Andorra             0     1                2
#>  3 2020-03-16 00:00:00 Andorra             0     3                5
#>  4 2020-03-17 00:00:00 Andorra             0     9               14
#>  5 2020-03-18 00:00:00 Andorra             0     0               14
#>  6 2020-03-19 00:00:00 Andorra             0    39               53
#>  7 2020-03-20 00:00:00 Andorra             0    22               75
#>  8 2020-03-21 00:00:00 Andorra             0     0               75
#>  9 2020-03-22 00:00:00 Andorra             0    13               88
#> 10 2020-03-23 00:00:00 Andorra             0    25              113
#> # … with 10,732 more rows, and 8 more variables: cumulative_deaths <dbl>,
#> #   year <dbl>, month <dbl>, week <dbl>, day <dbl>, geo_id <chr>,
#> #   countryterritory_code <chr>, pop_data2018 <dbl>

This contains new names for the data (from European CDC)

• dateRep –> date_rep
• countriesAndTerritories –> countries_and_territories
• geoId –> geo_id
• countryterritoryCode –> countryterritory_code
• popData2018 –> pop_data2018

For full use of the package and the data visualisations, we recommend that you look at the dashboard, ozcovis.