R routines that manipulate and display publicly available data about the Novel Coronavirus (SARS-CoV-2) and COVID-19. Most of the data are derived from Our World In Data, but publicly available death data from several countries are also used.
Other illustrative programs and data are used to contextualise the
coronavirus. I’ve also written a book (as yet unpublished) called
Rona: the autobiography of a virus that explores the data
in more depth and provides a rich social context.
Once installed (see below), from the R console say:
?coronaYou can similarly look up each function. Important examples are:
corona_country('New Zealand'): This graphs both
cases and deaths per day for the chosen country, but also provides
smoothed curves. The “5x death” curve is of special interest, as it
highlights differences between countries. Contrast ‘France’ and
‘Germany’, for example. For a list of countries, specify ‘?’.
country_dead(): Weekly deaths for various countries.
Defaults to ‘England+Wales’, but you can use the ‘?’ parameter to get a
list here too. Three sequential panels are displayed, first the raw
data, then with early adjustment for secular change, and finally a
control chart (created using qicharts2) that crudely adjusts
for seasonal variation, revealing the death spike where this is
prominent. (In contrast, in New Zealand, there is a ‘death
dip’).
Several other functions are available for exploring the context of the coronavirus. Some of these may at first glance seem irrelevant, but the book Rona ties them together in a rich social context. Topics covered include exponential growth (corona_rabbits), Bayesian decision making and the Monty Hall problem (corona_monty), the use of run charts to explore hand sanitisation and Semmelweis’ death data (corona_vienna), power laws (corona_metabolism) and how statistical distributions arise (corona_converge), with some notes on Benford’s law.
# Install the development version from GitHub
devtools::install_github("jvanschalkwyk/corona")