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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# covid19italy
<!-- badges: start -->
[](https://github.com/covid19r/covid19italy/actions?query=workflow%3Abuild)
[](https://cran.r-project.org/package=covid19italy)
[](https://www.tidyverse.org/lifecycle/#experimental)
[](https://opensource.org/licenses/MIT)
[](https://github.com/covid19r/covid19Italy/commit/master)
<!-- badges: end -->
The covid19italy R package provides a tidy format dataset of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) pandemic outbreak in Italy. The package includes the following three datasets:
- `italy_total` - daily summary of the outbreak on the national level
- `italy_region` - daily summary of the outbreak on the region level
- `italy_province` - daily summary of the outbreak on the province level
More information about the package datasets available [here](https://covid19r.github.io/covid19italy/articles/intro.html), and supporting dashboard available [here](https://ramikrispin.github.io/italy_dash/).
Data source: [Italy Department of Civil Protection](http://www.protezionecivile.it/)
[<img src="man/figures/Italy_province.png" width="100%" />](https://covid19r.github.io/covid19italy/articles/geospatial_visualization.html)
## Installation
You can install the released version of covid19italy from [CRAN](https://cran.r-project.org/package=covid19italy) with:
``` r
install.packages("covid19italy")
```
Or, install the most recent version from [GitHub](https://github.com/Covid19R/covid19italy) with:
``` r
# install.packages("devtools")
devtools::install_github("Covid19R/covid19Italy")
```
## Data refresh
While the **covid19italy** [CRAN version](https://cran.r-project.org/package=covid19italy) is updated every month or two, the [Github (Dev) version](https://github.com/Covid19R/covid19italy) is updated on a daily bases. The `update_data` function enables to overcome this gap and keep the installed version with the most recent data available on the Github version:
``` r
library(covid19italy)
update_data()
```
**Note:** must restart the R session to have the updates available
## Usage
```{r}
data(italy_total)
head(italy_total)
```
### Plotting the active cases distribution
``` r
library(plotly)
plot_ly(data = italy_total,
x = ~ date,
y = ~home_confinement,
name = 'Home Confinement',
fillcolor = '#FDBBBC',
type = 'scatter',
mode = 'none',
stackgroup = 'one') %>%
add_trace( y = ~ hospitalized_with_symptoms,
name = "Hospitalized with Symptoms",
fillcolor = '#E41317') %>%
add_trace(y = ~intensive_care,
name = 'Intensive Care',
fillcolor = '#9E0003') %>%
layout(title = "Italy - Distribution of Active Covid19 Cases",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Number of Cases"),
xaxis = list(title = "Source: Italy Department of Civil Protection"))
```
<img src="man/figures/positive_dist.png" width="100%" />
### Plotting the daily cases distribution
```r
plot_ly(data = italy_total,
x = ~ date,
y = ~ cumulative_positive_cases,
name = 'Active',
fillcolor = '#1f77b4',
type = 'scatter',
mode = 'none',
stackgroup = 'one') %>%
add_trace( y = ~ death,
name = "Death",
fillcolor = '#E41317') %>%
add_trace(y = ~recovered,
name = 'Recovered',
fillcolor = 'forestgreen') %>%
layout(title = "Italy - Distribution of Covid19 Cases",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Number of Cases"),
xaxis = list(title = "Source: Italy Department of Civil Protection"))
```
<img src="man/figures/case_dist.png" width="100%" />
### Cases distribution by region
``` r
italy_region %>%
filter(date == max(date)) %>%
select(region_name, cumulative_positive_cases, recovered, death, cumulative_cases) %>%
arrange(-cumulative_cases) %>%
mutate(region = factor(region_name, levels = region_name)) %>%
plot_ly(y = ~ region,
x = ~ cumulative_positive_cases,
orientation = 'h',
text = ~ cumulative_positive_cases,
textposition = 'auto',
type = "bar",
name = "Active",
marker = list(color = "#1f77b4")) %>%
add_trace(x = ~ recovered,
text = ~ recovered,
textposition = 'auto',
name = "Recovered",
marker = list(color = "forestgreen")) %>%
add_trace(x = ~ death,
text = ~ death,
textposition = 'auto',
name = "Death",
marker = list(color = "red")) %>%
layout(title = "Cases Distribution by Region",
barmode = 'stack',
yaxis = list(title = "Region"),
xaxis = list(title = "Number of Cases"),
hovermode = "compare",
legend = list(x = 0.65, y = 0.9),
margin = list(
l = 20,
r = 10,
b = 10,
t = 30,
pad = 2
))
```
<img src="man/figures/region_bar_plot.png" width="100%" />
### Cases distribution by province for Lombardia region
```r
italy_province %>%
filter(date == max(date), region_name == "Lombardia") %>%
plot_ly(labels = ~province_name, values = ~total_cases,
textinfo="label+percent",
type = 'pie') %>%
layout(title = "Lombardia - Cases Distribution by Province") %>%
hide_legend()
```
<img src="man/figures/province_pie.png" width="100%" />
## Supporting Dashboard
A supporting dashboard for the **covid19italy** datasets available [here](https://ramikrispin.github.io/italy_dash/).
<img src="man/figures/dashboard.png" width="100%" />