In RamiKrispin/covid19swiss: The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Cases in Switzerland and Principality of Liechtenstein

knitr::opts_chunk$set(
  collapse = TRUE,
  message=FALSE, 
  warning=FALSE,
  comment = "#>"
)

The covid19swiss R package provides a tidy format dataset of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) pandemic outbreak in Switzerland cantons and Principality of Liechtenstein (FL).

Data structure

The covid19swiss dataset includes the following fields:

• date - the timestamp of the case, a Date object
• location - the cantons of Switzerland and Principality of Liechtenstein (FL) abbreviation code
• location_type - description of the location, either Canton of Switzerland or the Principality of echtenstein
• location_code - a canton index code for merging geometry data from the rnaturalearth package, ailable only for Switzerland cantons
• location_code_type - the name of code in the rnaturalearth package for Switzerland map
• data_type - the type of case
• value - the number of cases corresponding to the date and data_type fields

Where the available data_type field includes the following cases:

• total_tested - number of tests performed as of the date
• total_confirmed - number of positive cases as of the date
• new_hosp - new hospitalizations with respect to the previously reported date
• current_hosp - number of hospitalized patients as of the current date
• current_icu - number of hospitalized patients in ICUs as of the current date
• current_vent - number of hospitalized patients requiring ventilation as of the current date
• total_recovered - total number of patients recovered as of the current date
• total_death - total number of death as of the current date

The data organized in a long format:

library(covid19swiss)

head(covid19swiss)

It is straightforward to transform the data into a wide format with the pivot_wider function from the tidyr package:

library(tidyr)

covid19swiss_wide <- covid19swiss %>% 
  pivot_wider(names_from = data_type, values_from = value)

head(covid19swiss_wide)

Query and summarise the data

The following examples demonstrate simple methods for query and summarise the data with the dplyr and tidyr packages.

Cases summary by canton

The first example demonstrates how to query the total confirmed, recovered, and death cases by canton as of April 8th:

library(dplyr)

covid19swiss %>%
  filter(date == as.Date("2020-04-10"),
         data_type %in% c("total_confirmed", "total_recovered", "total_death")) %>%
  select(location, value, data_type) %>%
  pivot_wider(names_from = data_type, values_from = value) %>%
  arrange(-total_confirmed)

Note: some fields, such as total_recovered or total_tested, are not available for some cantons and marked as missing values (i.e., NA)

Calculating rates for Canton of Geneva

In the next example, we will filter the dataset for the Canton of Geneva and calculate the following metrics:

• Positive rate - $\frac{Total ~confirmed}{Total ~tested}$
• Recovery rate - $\frac{Total ~recovered}{Total ~confirmed}$
• Death rate - $\frac{Total ~death}{Total ~confirmed}$

covid19swiss %>% dplyr::filter(location == "GE",
                               date == as.Date("2020-04-10")) %>%
  dplyr::select(data_type, value) %>%
  tidyr::pivot_wider(names_from = data_type, values_from = value) %>%
  dplyr::mutate(positive_tested = round(100 * total_confirmed / total_tested, 2),
                death_rate = round(100 * total_death / total_confirmed, 2),
                recovery_rate = round(100 * total_recovered / total_confirmed, 2)) %>%
  dplyr::select(positive_tested, recovery_rate, death_rate) 

Values are in precentage

Separating between Switzerland and Principality of Liechtenstein

The raw data include both Switzerland and the Principality of Liechtenstein. Separating the data by country can be done by using the location field:

switzerland <- covid19swiss %>% filter(location != "FL")

head(switzerland)

liechtenstein <- covid19swiss %>% filter(location == "FL")

head(liechtenstein)

RamiKrispin/covid19swiss documentation built on May 18, 2020, 11:54 a.m.