---
title: "Introduction to the covid19swiss Dataset"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction to the covid19italy Dataset}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
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 **the 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:

* `tested_total` - cumulative number of tests performed as of the date
* `cases_total` - cumulative confirmed Covid-19 cases as of the current date
* `hosp_new` - new hospitalizations on the current date
* `hosp_current` - current number of hospitalized patients as of the current date
* `icu_current` - number of hospitalized patients in ICUs as of the current date
* `vent_current` - number of hospitalized patients requiring ventilation as of the current date
* `recovered_total` - cumulative number of patients recovered as of the current date
* `deaths_total` - cumulative deaths due to Covid-19 as of the current date


The data organized in a long format:

```{r}
library(covid19swiss)

head(covid19swiss)
```


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

```{r}
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:

```{r }
library(dplyr)

covid19swiss %>%
  filter(date == as.Date("2020-09-08"),
         data_type %in% c("cases_total", "recovered_total", "death_total")) %>%
  select(location, value, data_type) %>%
  pivot_wider(names_from = data_type, values_from = value) %>%
  arrange(-cases_total)
```

**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 - $\frac{Total ~death}{Total ~confirmed}$



```{r}
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 * cases_total / tested_total, 2),
                death_rate = round(100 * deaths_total / cases_total, 2),
                recovery_rate = round(100 * recovered_total / cases_total, 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:

```{r}
switzerland <- covid19swiss %>% filter(location != "FL")

head(switzerland)

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

head(liechtenstein)
```