Guerry data: Maps with sf and ggplot2

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)
#> [1] "sf"         "data.frame"
names(gf_sf)
#>  [1] "CODE_DEPT"       "COUNT"           "AVE_ID_GEO"      "dept"           
#>  [5] "Region"          "Department"      "Crime_pers"      "Crime_prop"     
#>  [9] "Literacy"        "Donations"       "Infants"         "Suicides"       
#> [13] "MainCity"        "Wealth"          "Commerce"        "Clergy"         
#> [17] "Crime_parents"   "Infanticide"     "Donation_clergy" "Lottery"        
#> [21] "Desertion"       "Instruction"     "Prostitutes"     "Distance"       
#> [25] "Area"            "Pop1831"         "geometry"

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 (1999)) 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]”:

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.