## Monday, July 06, 2020

### Graphing COVID-19

There's been a lot of debate online about how various countries have fared in the face of the COVID-19 pandemic.  Often the countries chosen are cherry-picked, or the measure of deaths (i.e., totals, not rates) says more about the size of the countries' populations than their success or failure in any meaningful sense.

There are just 6 major countries with higher reported death rates than the US.  But the fact that all of them are well-functioning economies and democracies in Western Europe would tend to refute the notion that economic or political backwardness is the key to their high death rates.

In my opinion, the key disadvantages that the US and Western Europe have, relative to less wealthy countries and isolated islands like New Zealand, is that the former are so large and interconnected (in terms of travel).  A Western, especially US, reluctance to wear masks is also a major disadvantage relative to East Asian countries.

To illustrate such comparisons, I've written an R script to download the latest daily data from the European Centre for Disease Prevention and Control (ECDC), and turn daily death rates for selected countries into a line graph, such as here (click on graph for higher resolution; the graph is followed by my R script).

Note that Spain has revised figures downward for a reputed overcount of prior deaths; rather than go back and alter prior days' numbers, they did this by logging 1,918 "negative deaths" on May 25.  Later they revised numbers upward by 1,179 on June 19.
# **********************
# David Pittelli -- woodedpaths.blogspot.com

setwd("C:/Users/YourDirectory")
rm(list=ls())

library(ggplot2)
library(scales)
library(RColorBrewer)

ggplotColours <- 360="" function="" h="c(0," n="6," o:p="">
if ((diff(h)%%360) < 1) h <- -="" 360="" h="" n="" o:p="">
hcl(h = (seq(h, h, length = n)), c = 100, l = 65)

}

df = read.csv("https://opendata.ecdc.europa.eu/covid19/casedistribution/csv", na.strings = "", fileEncoding = "UTF-8-BOM", stringsAsFactors=FALSE)

# convert date info in format 'mm/dd/yyyy'
df\$date2 = as.Date(df\$dateRep, "%d/%m/%Y")

# Subset rows and add columns ***********************
# eliminate NAs in countryterritoryCode
df = df[!is.na(df\$countryterritoryCode),]

# Add deaths per 100 Million column
df\$DPC = (df\$deaths * 100000000) / df\$popData2019

# Add cases per 100 Million column
df\$CPC = (df\$cases * 100000000) / df\$popData2019

# vectors are reversed since MA can only look upwards (or in both directions)
df\$DPCMA = rev(filter(rev(df\$DPC), rep(1/7,7), sides = 1))
df\$CPCMA = rev(filter(rev(df\$CPC), rep(1/7,7), sides = 1))
df\$deathsMA = rev(filter(rev(df\$deaths), rep(1/7,7), sides = 1))

# Add day of week  and 1-7 *****************************
df\$dayOfWeek = weekdays(as.Date(df\$date2))
df\$dayNum = setNames(0:6, c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"))[weekdays(as.Date(df\$date2))]

# Save with a file name including today's date***********
today = paste(substr(date(), 5, 10), substr(date(), 21, 24))
filename = paste(today, "ECDC COVID table.csv")
write.csv(df, filename)

# Just look since March 1
df = df[df\$date2 > as.Date("2020-02-29"), ]

# Graph ***************************************
# Select Countries
df2 = df[df\$geoId == "ES" | df\$geoId == "IT" | df\$geoId == "UK" | df\$geoId == "SE" | df\$geoId == "US" | df\$geoId == "DE" |  df\$geoId == "KR"|  df\$geoId == "NZ",]

# Annotated plot, 7-day Moving Average, linear scale
p = ggplot(data = df2, aes(x = date2, y = DPCMA, group = geoId, color = geoId)) +
geom_line(size = 1.2) +
theme_bw() +
ggtitle(paste("Daily Death Rate from COVID-19, 7-Day smoothing,", today)) +
theme(plot.title = element_text(size = 24)) +
theme(axis.title.x = element_text(size=14)) +
theme(axis.title.y = element_text(size=14)) +
theme(axis.text.x = element_text(size=14)) +
theme(axis.text.y = element_text(size=14)) +
theme(legend.text = element_text(size = 14)) +
xlab("Date") + ylab("Daily Deaths per 100 Million People") +
theme(legend.title=element_blank())

# Manual colors; black for US
p +  scale_colour_manual(breaks=c("ES", "IT", "UK", "SE", "US", "DE", "KR", "NZ"), labels=c("Spain", "Italy", "UK", "Sweden", "US", "Germany", "S. Korea", "New Zealand"),
values =c(ggplotColours(7), "black")) +
annotate("text", x = as.Date(18425, origin="1970-01-01"), y = 1750, label="David Pittelli -- woodedpaths.blogspot.com\nData from ecdc.europa.eu", col = "black")