On this Picostat.com statistics page, you will find information about the Nightingale data set which pertains to Florence Nightingale's data on deaths from various causes in the Crimean War. The Nightingale data set is found in the HistData R package. Try to load the Nightingale data set in R by issuing the following command at the console data("Nightingale"). This may load the data into a variable called Nightingale. If R says the Nightingale data set is not found, you can try installing the package by issuing this command install.packages("HistData") and then attempt to reload the data with library("HistData") followed by data("Nightingale"). Perhaps strangley, if R gives you no output after entering a command, it means the command succeeded. If it succeeded you can see the data by typing Nightingale at the command-line which should display the entire dataset.

If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the Nightingale R data set. The size of this file is about 1,331 bytes.

Florence Nightingale's data on deaths from various causes in the Crimean War

Description

In the history of data visualization, Florence Nightingale is best remembered for her role as a social activist and her view that statistical data, presented in charts and diagrams, could be used as powerful arguments for medical reform.

After witnessing deplorable sanitary conditions in the Crimea, she wrote several influential texts (Nightingale, 1858, 1859), including polar-area graphs (sometimes called "Coxcombs" or rose diagrams), showing the number of deaths in the Crimean from battle compared to disease or preventable causes that could be reduced by better battlefield nursing care.

Her Diagram of the Causes of Mortality in the Army in the East showed that most of the British soldiers who died during the Crimean War died of sickness rather than of wounds or other causes. It also showed that the death rate was higher in the first year of the war, before a Sanitary Commissioners arrived in March 1855 to improve hygiene in the camps and hospitals.

Usage

data(Nightingale)

Format

A data frame with 24 observations on the following 10 variables.

Date

a Date, composed as as.Date(paste(Year, Month, 1, sep='-'), "%Y-%b-%d")

Month

Month of the Crimean War, an ordered factor

Year

Year of the Crimean War

Army

Estimated average monthly strength of the British army

Disease

Number of deaths from preventable or mitagable zymotic diseases

Wounds

Number of deaths directly from battle wounds

Other

Number of deaths from other causes

Disease.rate

Annual rate of deaths from preventable or mitagable zymotic diseases, per 1000

Wounds.rate

Annual rate of deaths directly from battle wounds, per 1000

Other.rate

Annual rate of deaths from other causes, per 1000

Details

For a given cause of death, D, annual rates per 1000 are calculated as 12 * 1000 * D / Army, rounded to 1 decimal.

The two panels of Nightingale's Coxcomb correspond to dates before and after March 1855

Source

The data were obtained from:

Pearson, M. and Short, I. (2007). Understanding Uncertainty: Mathematics of the Coxcomb. http://understandinguncertainty.org/node/214.

References

Nightingale, F. (1858) Notes on Matters Affecting the Health, Efficiency, and Hospital Administration of the British Army Harrison and Sons, 1858

Nightingale, F. (1859) A Contribution to the Sanitary History of the British Army during the Late War with Russia London: John W. Parker and Son.

Small, H. (1998) Florence Nightingale's statistical diagrams http://www.florence-nightingale-avenging-angel.co.uk/GraphicsPaper/Graphics.htm

Pearson, M. and Short, I. (2008) Nightingale's Rose (flash animation). http://understandinguncertainty.org/files/animations/Nightingale11/Nightingale1.html

Examples

data(Nightingale)# For some graphs, it is more convenient to reshape death rates to long format
#  keep only Date and death rates
require(reshape)
Night<- Nightingale[,c(1,8:10)]
melted <- melt(Night, "Date")
names(melted) <- c("Date", "Cause", "Deaths")
melted$Cause <- sub("\\.rate", "", melted$Cause)
melted$Regime <- ordered( rep(c(rep('Before', 12), rep('After', 12)), 3), 
                          levels=c('Before', 'After'))
Night <- melted# subsets, to facilitate separate plotting
Night1 <- subset(Night, Date < as.Date("1855-04-01"))
Night2 <- subset(Night, Date >= as.Date("1855-04-01"))# sort according to Deaths in decreasing order, so counts are not obscured [thx: Monique Graf]
Night1 <- Night1[order(Night1$Deaths, decreasing=TRUE),]
Night2 <- Night2[order(Night2$Deaths, decreasing=TRUE),]# merge the two sorted files
Night <- rbind(Night1, Night2)
require(ggplot2)
# Before plot
cxc1 <- ggplot(Night1, aes(x = factor(Date), y=Deaths, fill = Cause)) +
		# do it as a stacked bar chart first
   geom_bar(width = 1, position="identity", stat="identity", color="black") +
		# set scale so area ~ Deaths	
   scale_y_sqrt() 
		# A coxcomb plot = bar chart + polar coordinates
cxc1 + coord_polar(start=3*pi/2) + 
	ggtitle("Causes of Mortality in the Army in the East") + 
	xlab("")# After plot
cxc2 <- ggplot(Night2, aes(x = factor(Date), y=Deaths, fill = Cause)) +
   geom_bar(width = 1, position="identity", stat="identity", color="black") +
   scale_y_sqrt()
cxc2 + coord_polar(start=3*pi/2) +
	ggtitle("Causes of Mortality in the Army in the East") + 
	xlab("")## Not run: 
# do both together, with faceting
cxc <- ggplot(Night, aes(x = factor(Date), y=Deaths, fill = Cause)) +
 geom_bar(width = 1, position="identity", stat="identity", color="black") + 
 scale_y_sqrt() +
 facet_grid(. ~ Regime, scales="free", labeller=label_both)
cxc + coord_polar(start=3*pi/2) +
	ggtitle("Causes of Mortality in the Army in the East") + 
	xlab("")## End(Not run)## What if she had made a set of line graphs?# these plots are best viewed with width ~ 2 * height 
colors <- c("blue", "red", "black")
with(Nightingale, {
	plot(Date, Disease.rate, type="n", cex.lab=1.25, 
		ylab="Annual Death Rate", xlab="Date", xaxt="n",
		main="Causes of Mortality of the British Army in the East");
	# background, to separate before, after
	rect(as.Date("1854/4/1"), -10, as.Date("1855/3/1"), 
		1.02*max(Disease.rate), col=gray(.90), border="transparent");
	text( as.Date("1854/4/1"), .98*max(Disease.rate), "Before Sanitary\nCommission", pos=4);
	text( as.Date("1855/4/1"), .98*max(Disease.rate), "After Sanitary\nCommission", pos=4);
	# plot the data
	points(Date, Disease.rate, type="b", col=colors[1], lwd=3);
	points(Date, Wounds.rate, type="b", col=colors[2], lwd=2);
	points(Date, Other.rate, type="b", col=colors[3], lwd=2)
	}
)
# add custom Date axis and legend
axis.Date(1, at=seq(as.Date("1854/4/1"), as.Date("1856/3/1"), "3 months"), format="%b %Y")
legend(as.Date("1855/10/20"), 700, c("Preventable disease", "Wounds and injuries", "Other"),
	col=colors, fill=colors, title="Cause", cex=1.25)# Alternatively, show each cause of death as percent of total
Nightingale <- within(Nightingale, {
	Total <- Disease + Wounds + Other
	Disease.pct <- 100*Disease/Total
	Wounds.pct <- 100*Wounds/Total
	Other.pct <- 100*Other/Total
	})colors <- c("blue", "red", "black")
with(Nightingale, {
	plot(Date, Disease.pct, type="n",  ylim=c(0,100), cex.lab=1.25,
		ylab="Percent deaths", xlab="Date", xaxt="n",
		main="Percentage of Deaths by Cause");
	# background, to separate before, after
	rect(as.Date("1854/4/1"), -10, as.Date("1855/3/1"), 
		1.02*max(Disease.rate), col=gray(.90), border="transparent");
	text( as.Date("1854/4/1"), .98*max(Disease.pct), "Before Sanitary\nCommission", pos=4);
	text( as.Date("1855/4/1"), .98*max(Disease.pct), "After Sanitary\nCommission", pos=4);
	# plot the data
	points(Date, Disease.pct, type="b", col=colors[1], lwd=3);
	points(Date, Wounds.pct, type="b", col=colors[2], lwd=2);
	points(Date, Other.pct, type="b", col=colors[3], lwd=2)
	}
)
# add custom Date axis and legend
axis.Date(1, at=seq(as.Date("1854/4/1"), as.Date("1856/3/1"), "3 months"), format="%b %Y")
legend(as.Date("1854/8/20"), 60, c("Preventable disease", "Wounds and injuries", "Other"),
	col=colors, fill=colors, title="Cause", cex=1.25)

--

Dataset imported from https://www.r-project.org.