3 min read

Customized Covid-19 news

I recently read a newspaper headline that read “California and Some Other States See Coronavirus Cases Rise” and thought “I already knew that!” This was because I had been dabbling with the data to make my own plots.

An interesting aspect of the COVID-19 pandemic is that data-based reporting has moved to the fore. Nonetheless, I often find the reporting inadequate, not because it’s bad, but because what I am interested in understanding isn’t necessarily what is being reported.

But, with a modicum of data skills, it is easy to do your own reporting. For example, the New York Times provides COVID-19 data by state here. And Our World in Data has extensive data on COVID-19 around the world.

The two key data variables are cases and deaths. As a measure of progress of the pandemic, cases are a more timely statistic than deaths (and also a little less morbid), but there are by-now-well-understood issues with cases, such as under-reporting due to lack of symptoms, or symptoms insufficiently serious to lead to hospitalization or testing. As pointed out in the WSJ article linked to above, as testing capacity increases, we might expect to see a rise in reported cases, even if the underlying number of cases is flat.

Getting the data

Getting the data is quite easy.

library(dplyr, warn.conflicts = FALSE)

raw <- read_csv(paste0("https://raw.githubusercontent.com/",

covid_world_raw <- read_csv(paste0("https://covid.ourworldindata.org",
                            col_types = cols(.default = col_guess(),
                                             new_tests = col_double(),
                                             new_tests_smoothed = col_double(),
                                             new_tests_smoothed_per_thousand = col_double(),
                                             tests_per_case =  col_double(),
                                             positive_rate =  col_double(),
                                             total_tests = col_double(),
                                             total_tests_per_thousand = col_double(),
                                             new_tests_per_thousand = col_double(),
                                             tests_units = col_character()))
## Warning: 23144 parsing failures.
##  row                       col           expected actual                                                        file
## 2905 icu_patients              1/0/T/F/TRUE/FALSE 215.0  'https://covid.ourworldindata.org/data/owid-covid-data.csv'
## 2905 icu_patients_per_million  1/0/T/F/TRUE/FALSE 23.872 'https://covid.ourworldindata.org/data/owid-covid-data.csv'
## 2905 hosp_patients             1/0/T/F/TRUE/FALSE 856.0  'https://covid.ourworldindata.org/data/owid-covid-data.csv'
## 2905 hosp_patients_per_million 1/0/T/F/TRUE/FALSE 95.044 'https://covid.ourworldindata.org/data/owid-covid-data.csv'
## 2906 icu_patients              1/0/T/F/TRUE/FALSE 219.0  'https://covid.ourworldindata.org/data/owid-covid-data.csv'
## .... ......................... .................. ...... ...........................................................
## See problems(...) for more details.
covid_states <-
  raw %>%
  group_by(state) %>%
  arrange(date) %>%
  mutate(new_cases = cases - lag(cases),
         new_deaths = deaths - lag(deaths)) %>%
  rename(total_deaths = deaths,
         total_cases = cases)

Initially, I wanted to consider Australia as a US state for the purpose of comparison. For the US, I focused on a few states of interest: New York was the worst-hit state, Massachusetts is where I am now, and California is the most populous state. Pennsylvania provides an interesting comparison for Massachusetts. Apart from Australia, I also considered the United Kingdom, which was hit at about the same time as New York.

selected_states <- c("Massachusetts", "New York", "Pennsylvania", "California")
selected_countries <- c("AUS", "GBR")

covid_aus_usa <-
  covid_world_raw %>%
  filter(iso_code %in% selected_countries) %>%
  select(location:new_deaths) %>%
  rename(state = location)
covid_selected <-
  covid_states %>%
  filter(state %in% selected_states) %>%


One thing I noticed initially was a definite lumpiness to the data (e.g., many more deaths on Tuesdays rather than Sundays in Pennsylvania), which I assume is down to reporting rather than actual events. Initially, I used a four-day moving average, but here I use a seven-day moving average (as the four-day one still showed clean peaks and valleys).

covid_selected %>%
  group_by(state) %>%
  arrange(date) %>%
  mutate(new_cases = roll_meanr(new_cases, n = 7, fill = NA)) %>%
  filter(!is.na(new_cases)) %>%
  ggplot(aes(x = date, y = new_cases, color = state)) +
  geom_line() +
  scale_x_date(breaks = "4 weeks", date_minor_breaks = "1 week")


# So use a seven-day rolling average
covid_selected %>%
  group_by(state) %>%
  arrange(date) %>%
  mutate(new_deaths = roll_meanr(new_deaths, n = 7, fill = NA)) %>%
  filter(!is.na(new_deaths)) %>%
  ggplot(aes(x = date, y = new_deaths, color = state)) +
  geom_line() +
  scale_x_date(breaks = "4 weeks", date_minor_breaks = "1 week")