Minard’s Diagram of Napoleon’s Invasion of Russia in R

This is a recreation of Charles Minard's 1869 chart showing the number of men in Napoleon’s 1812 Russian campaign army, their movements and the temperature they encountered on their return path.

1. Data

We’re using three distinct CSV files for cities, troop movements and temperatures, all courtesy of Michael Friendly’s Re-Visions of Minard.

cities.csv

troops.csv

temps.csv

2. Plotting the data

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We start out with plotting the troop movements using the ggplot2 package. Let’s add it to our environment first.

install.packages("ggplot2")
library(ggplot2)

20.7s

ggplot2, gdtools, svglite,....

Next up, we look at the contents of and assign them to a troops variable.

troops <- read.table(troops.csv
, header = TRUE, stringsAsFactors = FALSE)

1.1s

All of the contained variables will be used for this plot:

the troops’ paths will be drawn using long and lat, each corresponding to the x and y axes
since Napoleon had various groups in the field, each group will have its own path
the paths will be drawn in different colors for both directions: advancement (A) and retreat (R)
the width of the paths will correspond to the number of survivors

ggsave("/results/minard-1.svg",
       ggplot(troops, aes(x = long, y = lat,
			                    group = group, color = direction,
  	   		                size = survivors)) + geom_path(),
       width = 7, height = 2.2)

1.0s

This looks already familiar but there are still some things to do:

We want the style and colors look similar to what’s in Minard’s diagram.
Minard chose to conflate the paths of the different groups starting their approach. This adds more drama to the graph. You see how the army starts out as one very wide line that is reduced to a sliver as they return.
We need to show the cities over the troops path.
We still need to show the temperater graph below the troops path.

ggsave("/results/minard-2.svg",
       ggplot(troops, aes(x = long, y = lat, group = group,
			                    color = direction, size = survivors)) +
		 	 	geom_path(lineend = "round") +
			  scale_size(range = c(0.5, 15)) + 
			  scale_colour_manual(values = c("#e5cbaa", "#242021")) +
			  labs(x = NULL, y = NULL) + 
			  guides(color = FALSE, size = FALSE) + theme_void(),
       width = 7, height = 2.2)

0.9s

Next up, we want to add the city labels. For this, we’ll read the contents of into a cities variable and use the ggrepel library to lay them out in a way that they don’t overlap. We also add extrafonts for some additional font options.

cities <- read.table(cities.csv
, header = TRUE, stringsAsFactors = FALSE)

1.0s

install.packages(c("ggrepel", "extrafont"))
library(ggrepel)
library(extrafont)
font_import()

28.7s

ggplot() +
	geom_path(data = troops,
            aes(x = long, y = lat, group = group,
                color = direction, size = survivors),
						lineend = "round") +
	geom_label_repel(data = cities,
                   aes(x = long, y = lat, label = city),
									 color = "#000000", size = 2, family = "Helvetica") +
	scale_size(range = c(0.5, 15)) + 
	scale_colour_manual(values = c("#e5cbaa", "#242021")) +
	labs(x = NULL, y = NULL) + 
	guides(color = FALSE, size = FALSE) +
	theme_void() +
	theme(aspect.ratio = 0.3)

1.1s

list(), list(<environment>....

Okay, next we’ll add the temperature graph starting by reading into a temps variable.

temps <- read.table(temps.csv
, header = TRUE, stringsAsFactors = FALSE)

1.1s

In , date is actually of type string so we have to convert that to actual dates. For that we use the lubridate package to parse the strings and convert them to dates.

install.packages("lubridate")
library(lubridate)

28.6s