Minard: Data from Minard's famous graphic map of Napoleon's march on...

In HistData: Data Sets from the History of Statistics and Data Visualization

Data from Minard's famous graphic map of Napoleon's march on Moscow

Description

Charles Joseph Minard's graphic depiction of the fate of Napoleon's Grand Army in the Russian campaign of 1815 has been called the "greatest statistical graphic ever drawn" (Tufte, 1983). Friendly (2002) describes some background for this graphic, and presented it as Minard's Challenge: to reproduce it using modern statistical or graphic software, in a way that showed the elegance of some computer language to both describe and produce this graphic.

Usage

data(Minard.troops)
data(Minard.cities)
data(Minard.temp)

Format

Minard.troops: A data frame with 51 observations on the following 5 variables giving the number of surviving troops.

long

Longitude

lat

Latitude

survivors

Number of surviving troops, a numeric vector

direction

a factor with levels A ("Advance") R ("Retreat")

group

a numeric vector

Minard.cities: A data frame with 20 observations on the following 3 variables giving the locations of various places along the path of Napoleon's army.

long

Longitude

lat

Latitude

city

City name: a factor with levels Bobr Chjat ... Witebsk Wixma

Minard.temp: A data frame with 9 observations on the following 4 variables, giving the temperature at various places along the march of retreat from Moscow.

long

Longitude

temp

Temperature

days

Number of days on the retreat march

date

a factor with levels Dec01 Dec06 Dec07 Nov09 Nov14 Nov28 Oct18 Oct24

Details

date in Minard.temp should be made a real date in 1815.

Source

https://www.cs.uic.edu/~wilkinson/TheGrammarOfGraphics/minard.txt

References

Friendly, M. (2002). Visions and Re-visions of Charles Joseph Minard, Journal of Educational and Behavioral Statistics, 27, No. 1, 31-51.

Friendly, M. (2003). Re-Visions of Minard. http://datavis.ca/gallery/re-minard.html

Examples

data(Minard.troops)
data(Minard.cities)
data(Minard.temp)

## Not run: 
#' ## Load required packages
require(ggplot2)
require(scales)
require(gridExtra)

#' ## plot path of troops, and another layer for city names
 plot_troops <- ggplot(Minard.troops, aes(long, lat)) +
		geom_path(aes(size = survivors, colour = direction, group = group),
                 lineend = "round", linejoin = "round")
 plot_cities <- geom_text(aes(label = city), size = 4, data = Minard.cities)
 
#' ## Combine these, and add scale information, labels, etc.
#' Set the x-axis limits for longitude explicitly, to coincide with those for temperature

breaks <- c(1, 2, 3) * 10^5 
plot_minard <- plot_troops + plot_cities +
 	scale_size("Survivors", range = c(1, 10), 
 	            breaks = breaks, labels = scales::comma(breaks)) +
  scale_color_manual("Direction", 
                     values = c("grey50", "red"), 
                     labels=c("Advance", "Retreat")) +
  coord_cartesian(xlim = c(24, 38)) +
  xlab(NULL) + 
  ylab("Latitude") + 
  ggtitle("Napoleon's March on Moscow") +
  theme_bw() +
  theme(legend.position=c(.8, .2), legend.box="horizontal")
 
#' ## plot temperature vs. longitude, with labels for dates
plot_temp <- ggplot(Minard.temp, aes(long, temp)) +
	geom_path(color="grey", size=1.5) +
	geom_point(size=2) +
	geom_text(aes(label=date)) +
	xlab("Longitude") + ylab("Temperature") +
	coord_cartesian(xlim = c(24, 38)) + 
	theme_bw()
	

#' The plot works best if we  re-scale the plot window to an aspect ratio of ~ 2 x 1
# windows(width=10, height=5)

#' Combine the two plots into one
grid.arrange(plot_minard, plot_temp, nrow=2, heights=c(3,1))


## End(Not run)

HistData documentation built on Aug. 10, 2023, 1:08 a.m.