Plot the weekday variation in reported COVID-19 deaths in the United States

weekday_effect_on_reported_COVID_deaths.R

library(readr)
library(ggdist)
library(tidyverse)
library(magrittr)
library(cowplot)
library(ISOweek)

df <- readr::read_csv("https://github.com/nytimes/covid-19-data/raw/master/rolling-averages/us.csv")
df %<>% mutate(wday = lubridate::wday(lubridate::ymd(date)))
weekdays <- c('Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday')

# write a function to help us render similar plot layouts for different variables
make_plot <- function(y_variable, y_title) {
  df %>% 
  filter(date >= lubridate::ymd('2020-03-01')) %>% 
  ggplot(aes(x = factor(wday, labels = weekdays), y = {{ y_variable }}, fill = factor(wday))) + 
  stat_dots(side = 'right', color = NA) +
  stat_halfeye(
  aes(
    fill = factor(wday),
    fill_ramp = stat(cut_cdf_qi(
      cdf, 
      .width = c(.5, .8, .95),
      labels = scales::percent_format()
    ))
  ),
  alpha = 0.6,
  interval_color = NA,
  position = "dodgejust",
  side = 'left'
  )+  
  scale_fill_viridis_d(end = 0.8) + 
  scale_fill_ramp_discrete(range = c(1, 0.2), na.translate = FALSE) +
  labs(
    x = "Day of the Week",
    y = y_title
  ) + 
  guides(fill = 'none', fill_ramp = 'none') + 
  labs(
    title = "Day of Week Effect on Daily COVID-19 Deaths in the United States", 
    subtitle = "Colored dots show the distribution, filled areas show 50%, 80%, and 95% intervals, black dots show weekday averages",
    caption = stringr::str_c(
      "@_christiantesta\n",
      "data period: 2020-03-01 through ", max(df$date), "\n",
      "https://github.com/nytimes/covid-19-data/blob/master/rolling-averages/us.csv"
      )
  ) + 
  cowplot::theme_cowplot() + 
  scale_y_continuous(labels = scales::comma_format(), breaks = c(0, 1e3, 2e3, 3e3, 4e3, 5e3)) + 
  theme(axis.text.x = element_text(angle = 75, hjust = 1))
}

# plot daily number of deaths by weekday
plt <- make_plot(deaths, "Daily Number of Reported Deaths")
ggsave(plot = plt, "weekday_effect_on_COVID_deaths.png", width = 10, height = 8)

# plot daily number of deaths by weekday on log scale
plt <- plt + 
  scale_y_continuous(trans = 'log', labels = scales::comma_format(), breaks = c(1, 10, 1e2, 1e3, 2e3, 3e3, 4e3, 5e3)) +
  labs(y = "Daily Number of Reported Deaths, Log Scale") 
ggsave(plot = plt, "weekday_effect_on_COVID_deaths_logscale.png", width = 10, height = 8)

# calculate comparisons of daily death counts to each weekly average –
# note that this is a weekly average for each calendar week, not a 7-day moving average
df %<>% mutate(ISOweek = ISOweek(date))
df %<>% group_by(ISOweek) %>% 
  mutate(difference_from_weekly_mean = deaths - mean(deaths),
  pct_from_weekly_mean = (deaths - mean(deaths))/mean(deaths))

# plot the difference in daily deaths from each week's average daily deaths
plt <- 
  make_plot(difference_from_weekly_mean, 'Daily Deaths Beyond the Weekly Average of Daily Deaths') + 
  scale_y_continuous(labels = scales::comma_format(), breaks = c(-2e3, -1e3, 0, 1e3, 2e3))
ggsave(plot = plt, "weekday_effect_on_COVID_deaths_difference.png", width = 10, height = 8)

# plot the percent difference in daily deaths from each week's average daily deaths
plt <- 
  make_plot(pct_from_weekly_mean, 'Percent of Deaths Over the Weekly Average of Daily Deaths') + 
  scale_y_continuous(labels = scales::percent_format()) + 
  background_grid(major = 'y', minor = 'y') 
ggsave(plot = plt, "weekday_effect_on_COVID_deaths_percent.png", width = 10, height = 8)

The effect the day-of-the-week or the weekend have on reported COVID-19 statistics has been discussed before in places such as ¹²³⁴.

↑ This is originally from ¹, also reported on in ²³, showing roughly March - November 2020. What I like about this is that by separating the weekday from weekend in this time-series style, we can see the weekend effect relative to that week's death data.

↑ These are from ⁴, and while I think they're definitely helpful visualizations, I enjoy that my visualizations also depict the variation within the data.

Footnotes

1. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7990432/ (Accessed February 14 2022) ↩ ↩²

2. https://consultqd.clevelandclinic.org/the-weekend-effect-and-covid-19-mortality/ (Accessed February 12 2022) ↩ ↩²

3. https://covidtracking.com/analysis-updates/how-day-of-week-effects-impact-covid-19-data (Accessed February 12 2022) ↩ ↩²

4. https://dearpandemic.org/covid-19-numbers-lower-on-the-weekends/ (Accessed February 14 2022) ↩ ↩²