Description Usage Format Details Source References Examples
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 Chalenge: 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.
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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
date
in Minard.temp
should be made a real date in 1815.
http://www.cs.uic.edu/~wilkinson/TheGrammarOfGraphics/minard.txt
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://www.math.yorku.ca/SCS/Gallery/re-minard.html
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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)
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