{tidycovid19}: New data and documentation

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A recent update to the {tidycovid19} package brings data on testing, alternative case data, some regional data and proper data documentation. Using all this, you can use the package to explore the associations of (the lifting of) governmental measures, citizen behavior and the Covid-19 spread.

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:

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()
var_name var_def
iso3c Country name
country ISO3c country code as defined by ISO 3166-1 alpha-3
date Calendar date
confirmed Confirmed Covid-19 cases as reported by JHU CSSE (accumulated)
deaths Covid-19-related deaths as reported by JHU CSSE (accumulated)
recovered Covid-19 recoveries as reported by JHU CSSE (accumulated)
ecdc_cases Covid-19 cases as reported by ECDC (accumulated)
ecdc_deaths Covid-19-related deaths as reported by ECDC (accumulated)
total_tests Accumulated test counts as reported by Our World in Data
tests_units Definition of what constitutes a ‘test’
soc_dist Number of social distancing measures reported up to date by ACAPS, net of lifted restrictions
mov_rest Number of movement restrictions reported up to date by ACAPS, net of lifted restrictions
pub_health Number of public health measures reported up to date by ACAPS, net of lifted restrictions
gov_soc_econ Number of social and economic measures reported up to date by ACAPS, net of lifted restrictions
lockdown Number of lockdown measures reported up to date by ACAPS, net of lifted restrictions
apple_mtr_driving Apple Maps usage for driving directions, as percentage*100 relative to the baseline of Jan 13, 2020
apple_mtr_walking Apple Maps usage for walking directions, as percentage*100 relative to the baseline of Jan 13, 2020
apple_mtr_transit Apple Maps usage for public transit directions, as percentage*100 relative to the baseline of Jan 13, 2020
gcmr_retail_recreation Google Community Mobility Reports data for the frequency that people visit retail and recreation places expressed as a percentage*100 change relative to the baseline period Jan 3 – Feb 6, 2020
gcmr_grocery_pharmacy Google Community Mobility Reports data for the frequency that people visit grocery stores and pharmacies expressed as a percentage*100 change relative to the baseline period Jan 3 – Feb 6, 2020
gcmr_parks Google Community Mobility Reports data for the frequency that people visit parks expressed as a percentage*100 change relative to the baseline period Jan 3 – Feb 6, 2020
gcmr_transit_stations Google Community Mobility Reports data for the frequency that people visit transit stations expressed as a percentage*100 change relative to the baseline period Jan 3 – Feb 6, 2020
gcmr_workplaces Google Community Mobility Reports data for the frequency that people visit workplaces expressed as a percentage*100 change relative to the baseline period Jan 3 – Feb 6, 2020
gcmr_residential Google Community Mobility Reports data for the frequency that people visit residential places expressed as a percentage*100 change relative to the baseline period Jan 3 – Feb 6, 2020
gtrends_score Google search volume for the term ‘coronavirus’, relative across time with the country maximum scaled to 100
gtrends_country_score Country-level Google search volume for the term ‘coronavirus’ over a period starting Jan 1, 2020, relative across countries with the country having the highest search volume scaled to 100 (time-stable)
region Country region as classified by the World Bank (time-stable)
income Country income group as classified by the World Bank (time-stable)
population Country population as reported by the World Bank (original identifier ‘SP.POP.TOTL’, time-stable)
land_area_skm Country land mass in square kilometers as reported by the World Bank (original identifier ‘AG.LND.TOTL.K2’, time-stable)
pop_density Country population density as reported by the World Bank (original identifier ‘EN.POP.DNST’, time-stable)
pop_largest_city Population in the largest metropolian area of the country as reported by the World Bank (original identifier ‘EN.URB.LCTY’, time-stable)
life_expectancy Average life expectancy at birth of country citizens in years as reported by the World Bank (original identifier ‘SP.DYN.LE00.IN’, time-stable)
gdp_capita Country gross domestic product per capita, measured in 2010 US-$ as reported by the World Bank (original identifier ‘NY.GDP.PCAP.KD’, time-stable)
timestamp Date and time where data has been collected from authoritative sources

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

Governmental measures over time

The ACAPS data allows for a quick impression on governmental restrictions are implemented and lifted over time.

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))

Association between testing and deaths

Is there an association between testing during the fist 30 days of the spread and the amount of deaths that a country observes?

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."
  )

Regional variance in individual behavior

Do the social distancing measures work differently in East and West Germany?

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)

The same analysis for Apple Data (but without Berlin as it is classified as a city in Apple data)

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!

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