In RamiKrispin/covid19Italy: The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Italy Dataset

library(covid19italy)

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

covid19italy

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, and supporting dashboard available here.

Data source: Italy Department of Civil Protection

Installation

You can install the released version of covid19italy from CRAN with:

install.packages("covid19italy")

Or, install the most recent version from GitHub with:

# install.packages("devtools")
devtools::install_github("RamiKrispin/covid19Italy")

Data refresh

While the covid19italy CRAN version is updated every month or two, the Github (Dev) version 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:

library(covid19italy)

update_data()

Note: must restart the R session to have the updates available

Usage

data(italy_total)

head(italy_total)

Plotting the active cases distribution

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 = 0.8, y = 0.9),
         yaxis = list(title = "Number of Cases"),
         xaxis = list(title = "Source: Italy Department of Civil Protection"))

library(plotly)

p <- 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 = 0.8, y = 0.9),
         yaxis = list(title = "Number of Cases"),
         xaxis = list(title = "Source: Italy Department of Civil Protection"))


orca(p, "man/figures/positive_dist.svg")

Plotting the daily cases distribution

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"))

p <- 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"))

orca(p, "man/figures/case_dist.svg")

Cases distribution by region

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
         ))

``` {r include=FALSE} library(dplyr)

p <- 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 ))

orca(p, "man/figures/region_bar_plot.svg")

<img src="man/figures/region_bar_plot.svg" 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()

``` {r include=FALSE} library(dplyr) p <- 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()

orca(p, "man/figures/province_pie.svg") ```