The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

covid19italy

build CRAN_Status_Badge lifecycle License: MIT GitHub commit Data refresh

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:

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)
#>         date hospitalized_with_symptoms intensive_care total_hospitalized home_confinement cumulative_positive_cases daily_positive_cases recovered death positive_clinical_activity
#> 1 2020-02-24                        101             26                127               94                       221                    0         1     7                         NA
#> 2 2020-02-25                        114             35                150              162                       311                   90         1    10                         NA
#> 3 2020-02-26                        128             36                164              221                       385                   74         3    12                         NA
#> 4 2020-02-27                        248             56                304              284                       588                  203        45    17                         NA
#> 5 2020-02-28                        345             64                409              412                       821                  233        46    21                         NA
#> 6 2020-02-29                        401            105                506              543                      1049                  228        50    29                         NA
#>   positive_surveys_tests cumulative_cases total_tests total_people_tested new_intensive_care total_positive_molecular_test total_positive_rapid_antigen_test molecular_test rapid_antigen_test
#> 1                     NA              229        4324                  NA                 NA                            NA                                NA             NA                 NA
#> 2                     NA              322        8623                  NA                 NA                            NA                                NA             NA                 NA
#> 3                     NA              400        9587                  NA                 NA                            NA                                NA             NA                 NA
#> 4                     NA              650       12014                  NA                 NA                            NA                                NA             NA                 NA
#> 5                     NA              888       15695                  NA                 NA                            NA                                NA             NA                 NA
#> 6                     NA             1128       18661                  NA                 NA                            NA                                NA             NA                 NA

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"))
  

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"))

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
         )) 

Cases distribution by province for Lombardia region

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()

Supporting Dashboard

A supporting dashboard for the covid19italy datasets available here.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.