Installation

The Package is hosted on Github. As the underlying data sources change their format and access methods often, I have no plans to publish the package on CRAN for the time being. So, to install the package, you need to have the {remotes} package installed. Then, you can install and attach {tidycovid19} along with some other packages that we will need below by:

remotes::install_github("joachim-gassen/tidycovid19")
library(tidycovid19)
library(tidyverse)
library(ggrepel)
library(gghighlight)
library(zoo)

Included Data Sources

By now, the packages contains the code to download data from nine authoritative data sources:

• Covid-19 data from the Johns Hopkins University CSSE Github Repo. This data has developed to a standard resource for researchers and the general audience interested in assessing the global spreading of the virus. The data is provided at country and sub-country levels.
• Covid-19 case data provided by the European Centre for Disease Prevention and Control. The data is updated daily and contains the latest available public data on the number of new Covid-19 cases reported per day and per country.
• Testing data collected by the ‘Our World in Data’ team. This team systematically collects data on Covid-19 testing from multiple national sources.
• Government measures dataset provided by the Assessment Capacities Project (ACAPS). These data allow researchers to study the effect of non-pharmaceutical interventions on the development of the virus.
• Oxford Covid-19 Government Response Tracker, an alternative data source for governmental interventions.
• Mobility Trends Reports provided by Apple related to Covid-19. The data is provided at country and sub-country levels.
• Google COVID-19 Community Mobility Reports data. This data is available at the country, regional and U.S. county level.
• Google Trends data on the search volume for the term “coronavirus” This data can be used to assess the public attention to Covid-19 across countries and over time within a given country. The data is available at the country, regional and city level but availability varies across countries.
• Country level information provided by the World Bank. These data allow researchers to calculate per capita measures of the virus spread and to assess the association of macro-economic variables with the development of the virus.

Each data can be downloaded separately, using its specific download function (download_..._data()). By default, the functions will download the data from the authoritative source and provide some diagnostic messages and a short data description. You can shut up the functions by adding the silent = TRUE parameter. If you use the cached = TRUE parameter, the data will be downloaded from the Github repository of the package, speeding things up considerably. The data in the Github repository is updated daily.

download_merged_data() provides a country-day data frame pulling together data from various sources. The data frame tidycovid19_variable_definitions contains variable definitions for the merged data frame.

merged <- download_merged_data(silent = TRUE, cached = TRUE)
tidycovid19_variable_definitions %>%
  select(var_name, var_def) %>%
  kable() %>% 
  kable_styling()

Included Visualization Methods

Based on this data, you can use the visualizations functions of the package to quickly produce visuals of the spread. The function plot_covid19_spread() allows many customization options. See:

plot_covid19_spread(
  merged, type = "deaths", min_cases = 1000, edate_cutoff = 60, 
  cumulative = FALSE, change_ave = 7, 
  highlight = c("USA", "ESP", "ITA", "FRA", "GBR", "DEU", "BRA", "RUS", "TUR")  
)

To customize it, you can also spin up its shiny variant, customize the plot until it fits your needs and then export the code to the clipboard with a simple click.

shiny_covid19_spread()

For many countries, the function plot_covid19_spread() provides an alternative way on how to visualize the spread.

plot_covid19_stripes(merged, type = "deaths", min_cases = 1000, cumulative = FALSE)

And, if you like maps (who does not?), you can also visualize the spread that way.

map_covid19(merged)

By the way, if you provide multiple days to map_covid19() it will produce an animated map, but this will take a while to complete.

Some stuff that you can do with the data

acaps <- download_acaps_npi_data(cached = TRUE, silent = TRUE)
df <- acaps %>%
  rename(date = date_implemented) %>%
  mutate(nobs = 1*(log_type == "Introduction / extension of measures") -
           1*(log_type != "Introduction / extension of measures")) %>%
  select(iso3c, date, log_type, category, nobs) %>%
  filter(date <= "2020-05-10")

ggplot(df, aes(x = date, fill = category, weight = nobs)) +
  geom_histogram(data = df %>% filter(log_type == "Introduction / extension of measures"),
                 position = "stack", binwidth = 24*3600*7) +
  geom_histogram(data = df %>% filter(log_type != "Introduction / extension of measures"),
                 position = "stack", binwidth = 24*3600*7) +
  theme_minimal() +
  labs(title = "Implementation and Lifting of Interventions over Calendar Time",
       x = NULL,
       y = "Number of interventions",
       fill = "Intervention") +
  theme(legend.position = c(0.25, 0.8),
        legend.background = element_rect(fill = "white", color = NA),
        legend.title = element_text(size = 8),
        legend.text = element_text(size = 7))

early_tests <- merged %>%
  group_by(iso3c) %>%
  filter(population > 10e6) %>%
  filter(confirmed > 0) %>%
  filter(!all(is.na(total_tests))) %>%
  mutate(total_tests = na.approx(c(0, total_tests), rule = 2)[-1]) %>%
  filter(date - min(date) < 30) %>%
  summarise(early_tests = unique(1e5*max(total_tests, na.rm = TRUE)/population)) %>%
  filter(!is.na(early_tests))
  
deaths <- merged %>%  
  group_by(iso3c) %>%
  filter(deaths > 1000) %>%
  filter(population > 10e6) %>%
  summarise(deaths = unique(1e5*max(deaths, na.rm = TRUE)/population)) 

deaths %>% left_join(early_tests, by = "iso3c") %>%
  filter(!is.na(early_tests)) %>%
  ggplot(aes(x = early_tests, y = deaths)) + 
  geom_point() +
  theme_minimal() + 
  geom_label_repel(aes(label = iso3c)) +
  scale_x_log10() +
  scale_y_log10() +
  labs(
    x = "Tests within the first 30 days by 100,000 inhabitants (interpolated)",
    y = "Deaths per 100,000 inhabitants",
    caption = "Case data: JHU CSSE, Test data: Our World in Data."
  )

gcmr <- download_google_cmr_data(type = "country_region", cached = TRUE, silent = TRUE)

east_regions <- c("Berlin", "Brandenburg", "Mecklenburg-Vorpommern",
          "Saxony", "Saxony-Anhalt", "Thuringia")

df <- gcmr %>% 
  filter(iso3c == "DEU") %>%
  mutate(east = ifelse(region %in% east_regions, "East Germany", "West Germany")) %>%
  select(-iso3c, -region, -timestamp) %>%
  group_by(date, east) %>%
  summarise_all(mean)

ggplot(df, aes(x = date, y = retail_recreation, color = east)) +
  geom_line() +
  theme_minimal() +
  labs(
    x = NULL,
    y = "Percentage change of visits in retail shopping\nand recreational areas",
    caption = "Movement data: Google CMR."
  ) + 
  gghighlight(TRUE, label_key = east)

amtr <- download_apple_mtr_data(type = "country_region", cached = TRUE, silent = TRUE)

east_regions <- c("Brandenburg", "Mecklenburg-Vorpommern",
          "Saxony", "Saxony-Anhalt", "Thuringia")

df <- amtr %>% 
  filter(iso3c == "DEU") %>%
  mutate(east = ifelse(region %in% east_regions, "East Germany", "West Germany")) %>%
  select(-iso3c, -region, -timestamp) %>%
  group_by(date, east) %>%
  summarise_all(mean)

ggplot(df, aes(x = date, y = driving, color = east)) +
  geom_line() +
  theme_minimal() +
  labs(
    x = NULL,
    y = "Percentage change of Apple Map requests\nfor driving directions",
    caption = "Movement data: Apple MTR."
  ) + 
  gghighlight(TRUE, label_key = east)

Compare Apple Mobility Trend Reports across major European cities

For driving directions:

amtr <- download_apple_mtr_data(type = "country_city", cached = TRUE, silent = TRUE)

cities <- c("Berlin", "London", "Madrid", 
            "Paris", "Rome", "Stockholm")

df <- amtr %>% 
  filter(city %in% cities) %>%
  select(-iso3c, -timestamp) %>%
  group_by(date, city) %>%
  summarise_all(mean)

ggplot(df, aes(x = date, y = driving, color = city)) +
  geom_line() +
  theme_minimal() +
  labs(
    x = NULL,
    y = "Percentage change of Apple Map\nrequests for driving directions",
    caption = "Movement data: Apple MTR."
  ) + 
  gghighlight(TRUE, label_key = city)

And for public transport directions:

ggplot(df, aes(x = date, y = transit, color = city)) +
  geom_line() +
  theme_minimal() +
  labs(
    x = NULL,
    y = "Percentage change of Apple Map requests\nfor public transport directions",
    caption = "Movement data: Apple MTR."
  ) + 
  gghighlight(TRUE, label_key = city)

Wrapping Up

I hope that this quick walk through helped you to assess the old and new content of the {tidycovid19} package. There are some more use cases in the file example_code.R of the tidycovid19 Github repository.

Everybody: Enjoy, stay well and keep #FlattenTheCurve!