Introduction

In late 2019, an outbreak of respiratory illness emerged in Wuhan, China, caused by a novel coronavirus. Initially identified as the 2019 Novel Coronavirus (2019-nCoV), it was later officially named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The virus causes Coronavirus Disease 2019 (COVID-19), which led to a global pandemic as cases rapidly spread to countries around the world, including the United States.

As the outbreak intensified, public health agencies, governments, and research institutions mobilized to track and mitigate the spread of the virus. One of the primary organizations monitoring the pandemic was the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE), which compiled and disseminated real-time global COVID-19 data. This dataset became a crucial resource for researchers, policymakers, and the public to assess the trajectory of infections and deaths.

The coronavirus package was developed to provide a structured and accessible way to work with JHU CCSE’s data. The package compiles global COVID-19 case, death, and recovery counts in a tidy format, making it easier to analyze trends over time. Although JHU stopped updating its dataset in March 2023, it’s GitHub repo preserves daily historical snapshots of the pandemic, allowing us to examine country-level mortality trends up until that point.

In this report, we analyze the trajectory of cumulative deaths from COVID-19 across several countries to better understand how the pandemic evolved over time.

For our analysis, in addition to the coronavirus package, we will use the following packages for data wrangling and visualisation.

  • tidyverse for data wrangling and visualization
  • lubridate package for handling dates
  • glue package for constructing text strings
  • scales package for formatting axis labels
  • ggrepel package for pretty printing of country labels

We will make use of the DT package for interactive display of tabular output in the Appendix.

library(coronavirus) # devtools::install_github("RamiKrispin/coronavirus")
library(tidyverse)
library(lubridate)
library(glue)
library(scales)
library(ggrepel)
library(DT)

Data prep

The data frame called coronavirus in the coronavirus package provides a daily summary of the Coronavirus (COVID-19) cases by country. Each row in the data frame represents a country (or, where relevant, state/province). A full list of the countries in the data frame is provided in the Appendix. Note that the data provided in this package provides daily number of deaths, confirmed cases, and recovered cases. For this report, we will focus on the deaths.

We will start by making our selection for the countries we want to explore.

countries <- c(
  "China",
  "France",
  "United Kingdom",
  "US",
  "Turkey"
)

In the following code chunk we filter the data frame for deaths in the countries we specified above and calculate cumulative number of deaths. We will only visualise data since 10th confirmed death.

country_data <- coronavirus %>%
  # filter for deaths in countries of interest
  filter(
    type == "death",
    country %in% countries
  ) %>%
  # fix county labels for pretty plotting
  mutate(
    country = case_when(
      country == "United Kingdom" ~ "UK",
      TRUE ~ country
    )
  ) %>%
  # calculate number of total cases for each country and date
  group_by(country, date) %>%
  summarise(tot_cases = sum(cases)) %>%
  # arrange by date in ascending order
  arrange(date) %>%
  # record daily cumulative cases as cumulative_cases
  mutate(cumulative_cases = cumsum(tot_cases)) %>%
  # only use days since the 10th confirmed death
  filter(cumulative_cases > 9) %>%
  # record days elapsed, end date, and end label
  mutate(
    days_elapsed = as.numeric(date - min(date)),
    end_date     = if_else(date == max(date), TRUE, FALSE),
    end_label    = if_else(end_date, country, NA_character_)
  ) %>%
  # ungroup
  ungroup()
## `summarise()` has regrouped the output.
## ℹ Summaries were computed grouped by country and date.
## ℹ Output is grouped by country.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(country, date))` for per-operation grouping
##   (`?dplyr::dplyr_by`) instead.

We also need to take a note of the “as of date” for the data so that we can properly label our visualisation.

as_of_date <- country_data %>% 
  summarise(max(date)) %>% 
  pull()

as_of_date_formatted <- glue("{wday(as_of_date, label = TRUE)}, {month(as_of_date, label = TRUE)} {day(as_of_date)}, {year(as_of_date)}")

These data are as of Thu, Mar 9, 2023.

Visualisation

The following visualisation shows the number of cumulative cases vs. days elapsed since the 10th confirmed death in each country. The time span plotted for each country varies since some countries started seeing (and reporting) deaths from COVID-19 much later than others.

ggplot(data = country_data,
       mapping = aes(x = days_elapsed, 
                     y = cumulative_cases, 
                     color = country, 
                     label = end_label)) +
  # represent cumulative cases with lines
  geom_line(size = 0.7, alpha = 0.8) +
  # add points to line endings
  geom_point(data = country_data %>% filter(end_date)) +
  # add country labels, nudged above the lines
  geom_label_repel(nudge_y = 1, direction = "y", hjust = 1) + 
  # turn off legend
  guides(color = FALSE) +
  # use pretty colors
  scale_color_viridis_d() +
  # better formatting for y-axis
  scale_y_continuous(labels = label_comma()) +
  # use minimal theme
  theme_minimal() +
  # customize labels
  labs(
    x = "Days since 10th confirmed death",
    y = "Cumulative number of deaths",
    title = "Cumulative deaths from COVID-19, selected countries",
    subtitle = glue("Data as of", as_of_date_formatted, .sep = " "),
    caption = "Source: github.com/RamiKrispin/coronavirus"
  )

References

  1. Assignment Adapted from Mine Cetinkaya-Rundel’s Data Science in a Box

Appendix

A list of countries in the coronavirus data frame is provided below.