Guerry data: Maps with sf and ggplot2"

In Guerry: Maps, Data and Methods Related to Guerry (1833) "Moral Statistics of France"

knitr::opts_chunk$set(
  collapse = TRUE,
  warning = FALSE,
  message = FALSE,
  comment = "#>",
  fig.width = 6,
  fig.height = 6,
  dpi = 96
)

The maps in this package (gfrance, gfrance85) were originally built as sp SpatialPolygonsDataFrame objects, and the other vignettes and the README use the older sp::spplot() function to display them. The current standard toolchain for spatial visualization in R is the sf package, together with ggplot2::geom_sf(). This short vignette shows how to work with Guerry's map data that way.

This doesn't replace the sp/spplot() examples used elsewhere in the package -- both remain fully supported -- it's simply the modern alternative for anyone building on Guerry's map data in a ggplot2 workflow.

library(Guerry)
library(sf)
library(ggplot2)
library(dplyr)
library(tidyr)
data(gfrance85)
data(Guerry_ranks)

Converting to sf

Converting the package SpatialPolygons sp object to use the Simple Features, sf, representation is a single call to sf::st_as_sf():

gf_sf <- st_as_sf(gfrance85)
class(gf_sf)
names(gf_sf)

The result is an ordinary data frame -- all the usual Guerry variables are still there as columns -- with one addition: a geometry list-column, where each row holds the polygon (or multi-polygon) boundary for that department as an sfc object, instead of a separate @polygons slot as in the sp representation.

Because it's just a data frame, it can be manipulated with the usual tools (dplyr, tidyr, ...) before plotting, and geom_sf() knows to look for a geometry column automatically.

We're using gfrance85 here (rather than gfrance), so Corsica -- geographically distant from the mainland and excluded from the Region classification -- is not shown in any of the maps below.

A basic map

geom_sf() handles the geometry column automatically to draw the map. There's no need to supply x/y aesthetics. You can supply attributes like fill and color. For maps, theme_void() is often the most sensible choice for overall styling.

ggplot(gf_sf) +
  geom_sf(fill = "grey90", color = "white") +
  theme_void()

Choropleth maps

Mapping a variable to fill gives a choropleth map, shading the departments according to the values of that variable. This is a good example of how the "Grammar of Graphics" (@Wilkinson:99) helps you think about the task that Guerry worked on for each of his maps, laboriously translating the values of a moral variable into visible tints.

In this example, Literacy (percent of military conscripts who could read and write) is mapped to a PuBu palette, the same one used elsewhere in this package. Literacy is one of the variables Guerry recorded so that a higher value is morally better; following the convention of his own (black-and-white) maps, where "worse" printed darker, direction = -1 makes low (bad) values dark and high (good) values light.

This isn't just an assumed convention -- it's exactly what Guerry says himself. His original 1833 map of the closely related Instruction variable (Plate III of the Statistique morale) carries this footnote: "Dans cette carte, l'obscurité des teintes correspond au minimum de l'instruction" -- "In this map, the darkness of the tints corresponds to the minimum of instruction [literacy]":

knitr::include_graphics("../man/figures/Guerry1833-instruction.jpg")

ggplot(gf_sf) +
  geom_sf(aes(fill = Literacy), color = "white", linewidth = 0.2) +
  scale_fill_distiller(palette = "PuBu", direction = -1, name = "Literacy") +
  theme_void()

The historically low-literacy departments of Brittany and central France stand out as the darkest; the generally more literate northeast is lightest -- the same departments that are darkest in Guerry's own hand-tinted map above.

Note that the numbers on Guerry's map are his own rank labels for Instruction, where 1 is the best (lightest) department -- the opposite direction from Guerry_ranks in this package (see below), which is worth keeping in mind if you compare the two directly.

Small multiples of the main variables

The package README shows a static image of six choropleth maps of Guerry's main "moral variables". Here is a living, code-generated version of the same idea, using facet_wrap() and the pre-ranked Guerry_ranks data set.

Guerry_ranks gives each variable's plain ascending rank (dplyr::dense_rank(), so ties share a rank and the maximum rank can be less than 86): rank 1 is always the smallest raw value, and the largest rank is the largest raw value. Because these variables are already recoded so that a larger raw value is morally better (as above), rank 1 is the worst department on that variable, and the highest rank is the best -- the reverse of the raw-value case, even though it's the same underlying idea. To keep "worse = dark, better = light" consistent with the previous figure, direction has to flip along with it: direction = -1 on the rank scale makes the low rank (worst) dark and the high rank (best) light.

main_vars <- c("Crime_pers", "Crime_prop", "Literacy", "Donations", "Infants", "Suicides")

ranks_sf <- gf_sf |>
  select(dept) |>
  left_join(Guerry_ranks |> select(dept, all_of(main_vars)), by = "dept") |>
  pivot_longer(cols = all_of(main_vars), names_to = "variable", values_to = "rank")

ggplot(ranks_sf) +
  geom_sf(aes(fill = rank), color = NA) +
  facet_wrap(~ variable) +
  scale_fill_distiller(palette = "PuBu", direction = -1, name = "Rank") +
  theme_void() +
  theme(strip.text = element_text(size = 11, face = "bold"))

Comparing the Literacy panel here with the single-variable choropleth above confirms the two now agree: the same departments read dark (worse) and light (better) in both.

Region map with department labels

Finally, a map colored by Region, with department names added via geom_sf_text() -- the sf/ggplot2 equivalent of the plot() + text() approach used for this same map in the README.

col.region <- colors()[c(149, 254, 468, 552, 26)]  # same colors used in the README

ggplot(gf_sf) +
  geom_sf(aes(fill = Region), color = "white", linewidth = 0.3) +
  geom_sf_text(aes(label = Department, color = Region == "W"),
               size = 3.2, check_overlap = TRUE) +
  scale_color_manual(values = c(`FALSE` = "black", `TRUE` = "white"), guide = "none") +
  scale_fill_manual(values = col.region) +
  theme_void()

Next steps

A themed, styled version of these maps using the historical color palettes and patterns of the ggCheysson package is planned once that package reaches CRAN.

References

Guerry documentation built on Sept. 15, 2026, 1:09 a.m.