Introduction to the covid19tunisia package
In covid19tunisia: Cases of COVID-19 in Tunisia

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

The covid19tunisia R package provides a tidy format dataset of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) pandemic outbreak in Tunisia. The package covers a daily summary of the outbreak on the national level.

The data was pull from :

Official Facebook page of the Tunisian Ministry of Health through their daily published press releases.
National Observatory of New and Emerging Diseases
Regional governments in Tunisia.

Installation

You can install the released version of covid19tunisia from CRAN with:

install.packages("covid19tunisia")

Overview

The covid19tunisia dataset provides an overall summary of the cases in Tunisia since the beginning of the covid19 outbreak on March 2, 2020. The dataset contains the following fields:

▲ date - The date in YYYY-MM-DD form.

▲ location - The name of the government as provided by the data sources.

▲ location_type - The type of location using the covid19R controlled vocabulary. In this case, it's “state”.

▲ location_code - A standardized location code using a national or international standard. In this case, . See https://www.iso.org/obp/ui/#iso:code:3166:TN for details.

▲ location_code_type The type of standardized location code being used according to the covid19R controlled vocabulary. Here we use “ISO 3166-2”.

▲ data_type - the type of data in that given row. Includes cases new : new confirmed Covid-19 cases during on the current date, recovered_new : new number of patients recovered on the current date and deaths_new : new deaths on the current date.

▲ value - number of cases of each data type.

library(covid19tunisia)

data <- refresh_covid19tunisia()

head(data)

str(data)

Plotting the daily evolution of active cases

# Transform the data

library(dplyr)
library(tidyr)
library(plotly)

data_transformed <- data %>% group_by(date,data_type) %>% summarise(value=sum(value)) %>% 
                    spread(data_type,value)

head(data_transformed)
# A tibble: 6 x 4
# Groups:   date [6]
  date       cases_new deaths_new recovered_new
  <date>         <dbl>      <dbl>         <dbl>
1 2020-03-02         1          0             0
2 2020-03-08         1          0             0
3 2020-03-09         3          0             0
4 2020-03-10         1          0             0
5 2020-03-11         1          0             0
6 2020-03-12         6          0             0

  data_transformed %>%
  ungroup() %>% plot_ly(type = 'scatter', 
                        mode = 'lines+markers')%>% 
  add_trace(x = ~date, y = ~cumsum(cases_new), 
            name = 'Confirmed cases',
            marker = list(color = '#fec44f'),
            line = list(color = '#fec44f'),
            hoverinfo = "text",
            text = ~paste(cases_new, "New confirmed cases\n",cumsum(cases_new), 'Total number of infected cases on', date)) %>%
  add_trace(x = ~date, y = ~cumsum(deaths_new),
            name = 'Deaths',
            marker = list(color = 'red'),
            line = list(color = 'red'),
            hoverinfo = "text",
            text = ~paste(deaths_new, "New deaths\n",cumsum(deaths_new), 'Total number of deaths on', date)) %>%
  add_trace(x = ~date, y = ~cumsum(recovered_new), 
            name = 'Recovered cases',
            marker = list(color = 'green'),
            line = list(color = 'green'),
            hoverinfo = "text",
            text = ~paste(recovered_new, "New recovered cases\n",cumsum(recovered_new), 'Total number of recovered cases on', date)) %>% 
  layout(title = 'Tunisia - Daily Evolution of Active COVID19 Cases',
         legend = list(x = 0.1, y = 0.9,
                       font = list(family = "sans-serif", size = 14, color = "#000"), bgcolor = "",
                       bordercolor = "#FFFFFF", borderwidth = 2),
         xaxis = list(title = ""),
         yaxis = list(side = 'left', title = 'Daily evolution', showgrid = TRUE, zeroline = TRUE))