Mapping Guerry’s Moral Statistics with Cheysson Palettes

Preparing the Data

Load and Examine Guerry’s Data

# Load the dataset
data(Guerry, package = "Guerry")

# Key variables for mapping
vars_of_interest <- c("Crime_pers", "Crime_prop", "Literacy",
                      "Donations", "Infants", "Suicides")

# View summary
str(Guerry[, c("dept", "Department", vars_of_interest)])
#> 'data.frame':    86 obs. of  8 variables:
#>  $ dept      : int  1 2 3 4 5 7 8 9 10 11 ...
#>  $ Department: Factor w/ 86 levels "Ain","Aisne",..: 1 2 3 11 40 4 5 6 7 8 ...
#>  $ Crime_pers: int  28870 26226 26747 12935 17488 9474 35203 6173 19602 15647 ...
#>  $ Crime_prop: int  15890 5521 7925 7289 8174 10263 8847 9597 4086 10431 ...
#>  $ Literacy  : int  37 51 13 46 69 27 67 18 59 34 ...
#>  $ Donations : int  5098 8901 10973 2733 6962 3188 6400 3542 3608 2582 ...
#>  $ Infants   : int  33120 14572 17044 23018 23076 42117 16106 22916 18642 20225 ...
#>  $ Suicides  : int  35039 12831 114121 14238 16171 52547 26198 123525 10989 66498 ...

Load the Map

The gfrance85 object is a SpatialPolygonsDataFrame. We’ll convert it to an sf object for modern spatial handling.

# Load the map
data(gfrance85, package = "Guerry")

# Convert to sf object (simple features)
france_sf <- st_as_sf(gfrance85)

# Check structure
head(france_sf[, c("Department", "Region")])
#> Simple feature collection with 6 features and 2 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 595532 ymin: 1858801 xmax: 975716 ymax: 2564568
#> CRS:           NA
#>     Department Region                       geometry
#> 0          Ain      E MULTIPOLYGON (((801150 2092...
#> 1        Aisne      N MULTIPOLYGON (((729326 2521...
#> 2       Allier      C MULTIPOLYGON (((710830 2137...
#> 3 Basses-Alpes      E MULTIPOLYGON (((882701 1920...
#> 4 Hautes-Alpes      E MULTIPOLYGON (((886504 1922...
#> 5      Ardeche      S MULTIPOLYGON (((747008 1925...

Join Data with Map

# Convert variables to ranks (since they're on different scales)
guerry_ranked <- Guerry
for (var in vars_of_interest) {
  guerry_ranked[[paste0(var, "_rank")]] <- rank(guerry_ranked[[var]], na.last = "keep")
}

# Join with spatial data
france_data <- merge(france_sf, guerry_ranked,
                     by = "Department",
                     all.x = TRUE)

# Check the join
cat("Departments in map:", nrow(france_sf), "\n")
#> Departments in map: 85
cat("Departments with data:", sum(!is.na(france_data$Crime_pers_rank)), "\n")
#> Departments with data: 85

Creating Choropleth Maps

Crime Against Persons (Sequential Palette)

# Map of crimes against persons
p1 <- ggplot(france_data) +
  geom_sf(aes(fill = Crime_pers_rank), color = "black", linewidth = 0.3) +
  scale_fill_cheysson("1895_16", discrete = FALSE,
                      name = "Rank") +
  labs(
    title = "Crimes Against Persons",
    subtitle = "France, 1830s (ranked by department)",
    caption = "Data: André-Michel Guerry (1833)"
  ) +
  theme_cheysson_map() +
  theme(
    legend.position = "right"
  )

print(p1)

Property Crime (Different Sequential Palette)

p2 <- ggplot(france_data) +
  geom_sf(aes(fill = Crime_prop_rank), color = "black", linewidth = 0.3) +
  scale_fill_cheysson("1895_16", discrete = FALSE,
                      name = "Rank") +
  labs(
    title = "Crimes Against Property",
    subtitle = "France, 1830s (ranked by department)",
    caption = "Data: André-Michel Guerry (1833)"
  ) +
  theme_cheysson_map() +
  theme(
    legend.position = "right"
  )

print(p2)

Literacy (Grouped Palette)

# Create quintiles for discrete display
france_data$Literacy_quint <- cut(france_data$Literacy_rank,
                                  breaks = quantile(france_data$Literacy_rank,
                                                   probs = seq(0, 1, 0.2),
                                                   na.rm = TRUE),
                                  include.lowest = TRUE,
                                  labels = c("Lowest", "Low", "Medium", "High", "Highest"))

p3 <- ggplot(france_data) +
  geom_sf(aes(fill = Literacy_quint), color = "black", linewidth = 0.3) +
  scale_fill_cheysson("1881_22",
                      name = "Literacy\nQuintile",
                      na.value = "grey80") +
  labs(
    title = "Literacy Rates",
    subtitle = "Percent of military conscripts who can read & write (quintiles)",
    caption = "Data: André-Michel Guerry (1833)"
  ) +
  theme_cheysson_map() +
  theme(
    legend.position = "right"
  )

print(p3)

Literacy with Cheysson Patterns

Now let’s recreate the literacy map using Cheysson’s signature hatching patterns. Palette 1888_27 is one of his sequential hatching scales: diagonal stripes that get progressively denser, ending in a solid fill. Mapping pattern_spacing as well as pattern reproduces that light-to-dark progression across the five quintiles. The palette’s spacings were measured on Cheysson’s small swatches, so they are scaled down here to suit a full-page map:

# Literacy with patterns - quintessential Cheysson style
lit_spacing <- cheysson_pattern_params(cheysson_pattern("1888_27"), "pattern_spacing")

p3b <- ggplot(france_data) +
  geom_sf_pattern(
    aes(fill = Literacy_quint,
        pattern = Literacy_quint,
        pattern_fill = Literacy_quint,
        pattern_spacing = Literacy_quint),
    pattern_density = 0.3,
    pattern_colour = NA,
    color = "black",
    linewidth = 0.4
  ) +
  scale_fill_cheysson_pattern("1888_27", na.value = "grey90") +
  scale_pattern_fill_cheysson("1888_27", na.value = "grey90") +
  scale_pattern_type_cheysson("1888_27") +
  scale_pattern_spacing_manual(values = 0.3 * lit_spacing) +
  labs(
    title = "Literacy Rates",
    subtitle = "Sequential hatching, sparse to solid (quintiles)",
    caption = "Data: André-Michel Guerry (1833)"
  ) +
  theme_cheysson_map() +
  theme(
    legend.position = "right"
  ) +
  guides(
    fill = guide_legend(title = "Literacy\nQuintile"),
    pattern = guide_legend(title = "Literacy\nQuintile"),
    pattern_spacing = guide_legend(title = "Literacy\nQuintile"),
    pattern_fill = guide_legend(title = "Literacy\nQuintile")
  )

print(p3b)

Charitable Donations (Category Palette)

# Create categories
france_data$Donations_cat <- cut(france_data$Donations_rank,
                                 breaks = quantile(france_data$Donations_rank,
                                                  probs = seq(0, 1, 0.25),
                                                  na.rm = TRUE),
                                 include.lowest = TRUE,
                                 labels = c("Low", "Medium-Low", "Medium-High", "High"))

p4 <- ggplot(france_data) +
  geom_sf(aes(fill = Donations_cat), color = "black", linewidth = 0.3) +
  scale_fill_cheysson("1883_31",
                      name = "Donations\nLevel",
                      na.value = "grey80") +
  labs(
    title = "Charitable Donations",
    subtitle = "Donations to the poor (quartiles)",
    caption = "Data: André-Michel Guerry (1833)"
  ) +
  theme_cheysson_map() +
  theme(
    legend.position = "right"
  )

print(p4)

Donations with Cheysson Patterns

The combination of colors and patterns was a hallmark of the Albums. Palette 1883_31 is one of Cheysson’s diverging schemes: two hues, each used both solid and hatched. As in his maps, the solid fills mark the extremes and the hatched versions the milder classes on each side. Diverging palettes are stored in that low-to-high order, so the Cheysson pattern scales apply it directly:

# Donations: solid at the extremes, hatched in the middle
p4b <- ggplot(france_data) +
  geom_sf_pattern(
    aes(fill = Donations_cat,
        pattern = Donations_cat,
        pattern_fill = Donations_cat),
    pattern_density = 0.35,
    pattern_spacing = 0.025,
    pattern_colour = NA,
    color = "black",
    linewidth = 0.4
  ) +
  scale_fill_cheysson_pattern("1883_31", na.value = "grey90") +
  scale_pattern_fill_cheysson("1883_31", na.value = "grey90") +
  scale_pattern_type_cheysson("1883_31") +
  labs(
    title = "Charitable Donations",
    subtitle = "Authentic Cheysson-style patterns and colors (quartiles)",
    caption = "Data: André-Michel Guerry (1833)"
  ) +
  theme_cheysson_map() +
  theme(
    legend.position = "right"
  ) +
  guides(
    fill = guide_legend(title = "Donations\nLevel"),
    pattern = guide_legend(title = "Donations\nLevel"),
    pattern_fill = guide_legend(title = "Donations\nLevel")
  )

print(p4b)

Illegitimate Births (Sequential Palette)

p5 <- ggplot(france_data) +
  geom_sf(aes(fill = Infants_rank), color = "black", linewidth = 0.3) +
  scale_fill_cheysson("1891_25", discrete = FALSE,
                      name = "Rank") +
  labs(
    title = "Illegitimate Births",
    subtitle = "Population per illegitimate birth (ranked by department)",
    caption = "Data: André-Michel Guerry (1833)"
  ) +
  theme_cheysson_map() +
  theme(
    legend.position = "right"
  )

print(p5)

Suicides (Different Sequential Palette)

p6 <- ggplot(france_data) +
  geom_sf(aes(fill = Suicides_rank), color = "black", linewidth = 0.3) +
  scale_fill_cheysson("1887_22", discrete = FALSE,
                      name = "Rank") +
  labs(
    title = "Suicides",
    subtitle = "Annual suicides per population (ranked by department)",
    caption = "Data: André-Michel Guerry (1833)"
  ) +
  theme_cheysson_map() +
  theme(
    legend.position = "right"
  )

print(p6)

Small Multiples: Comparing Crime Types

Create a faceted map showing multiple variables at once:

# Prepare data in long format for faceting
library(tidyr)
library(dplyr)

crime_long <- france_data |>
  st_as_sf() |>
  select(Department, Crime_pers_rank, Crime_prop_rank,
         Literacy_rank, Suicides_rank) |>
  pivot_longer(cols = ends_with("_rank"),
               names_to = "Variable",
               values_to = "Rank") |>
  mutate(Variable = recode(Variable,
                          "Crime_pers_rank" = "Crimes Against Persons",
                          "Crime_prop_rank" = "Property Crimes",
                          "Literacy_rank" = "Literacy Rate",
                          "Suicides_rank" = "Suicides"))

p7 <- ggplot(crime_long) +
  geom_sf(aes(fill = Rank), color = "grey30", linewidth = 0.2) +
  scale_fill_cheysson("1895_16", discrete = FALSE,
                      name = "Rank") +
  facet_wrap(~ Variable, ncol = 2) +
  labs(
    title = "Social Statistics of France, 1830s",
    subtitle = "Four measures of moral statistics (ranked by department)",
    caption = "Data: André-Michel Guerry (1833)"
  ) +
  theme_cheysson_map() +
  theme(
    strip.background = element_rect(fill = "#edd493", color = "black"),
    strip.text = element_text(size = 10, face = "bold"),
    legend.position = "bottom",
    legend.key.width = unit(2, "cm")
  )

print(p7)

Regional Patterns

Let’s also examine regional patterns using discrete categories:

# Map showing regions
# Note: After merge, Region column may be duplicated as Region.x or Region.y
# We'll use the spatial data version (Region.x) or check which exists
region_col <- if("Region" %in% names(france_data)) {
  "Region"
} else if("Region.x" %in% names(france_data)) {
  "Region.x"
} else {
  "Region.y"
}

p8 <- ggplot(france_data) +
  geom_sf(aes(fill = .data[[region_col]]), color = "black", linewidth = 0.4) +
  scale_fill_cheysson("category",
                      name = "Region") +
  labs(
    title = "Regions of France",
    subtitle = "Administrative divisions circa 1830",
    caption = "Source: Guerry package"
  ) +
  theme_cheysson_map() +
  theme(
    legend.position = "right"
  )

print(p8)

Regions with Patterns: Classic Cheysson Cartography

One of Cheysson’s most distinctive techniques was using varied hatching patterns to distinguish regions. Most category palettes are solid colors only, so we pick one that has hatching: 1883_30 has red and blue stripes, a red-and-blue crosshatch, black stripes and solid black - one for each of the five regions. Mapping pattern_angle as well draws the stripes at the angles used in the original plate, and pattern_fill2 gives the crosshatch its second color.

# Regions with distinctive patterns - very characteristic of Cheysson
p8b <- ggplot(france_data) +
  geom_sf_pattern(
    aes(fill = .data[[region_col]],
        pattern = .data[[region_col]],
        pattern_fill = .data[[region_col]],
        pattern_fill2 = .data[[region_col]],
        pattern_angle = .data[[region_col]]),
    pattern_colour = NA,
    pattern_density = 0.3,
    pattern_spacing = 0.02,
    color = "black",
    linewidth = 0.5
  ) +
  scale_fill_cheysson_pattern("1883_30") +
  scale_pattern_fill_cheysson("1883_30") +
  scale_pattern_fill2_cheysson("1883_30") +
  scale_pattern_type_cheysson("1883_30") +
  scale_pattern_angle_cheysson("1883_30") +
  labs(
    title = "Regions of France",
    subtitle = "Distinctive hatching patterns for each region - authentic Albums style",
    caption = "Source: Guerry package",
    fill = "Region", pattern = "Region", pattern_fill = "Region",
    pattern_fill2 = "Region", pattern_angle = "Region"
  ) +
  theme_cheysson_map() +
  theme(
    legend.position = "right"
  )

print(p8b)

Bivariate Comparison

Compare two variables using different visual encodings:

# Create categories for both variables
france_data$Crime_cat <- cut(france_data$Crime_pers_rank,
                             breaks = 3,
                             labels = c("Low", "Medium", "High"))

france_data$Lit_cat <- cut(france_data$Literacy_rank,
                           breaks = 3,
                           labels = c("Low", "Medium", "High"))

# Create bivariate category
france_data$Bivariate <- paste0(france_data$Crime_cat, "\n",
                                france_data$Lit_cat, " Literacy")

# Plot
p9 <- ggplot(france_data) +
  geom_sf(aes(fill = Crime_pers_rank), color = "black", linewidth = 0.5) +
  scale_fill_cheysson("1895_16", discrete = FALSE, name = "Crime\nRank") +
  # Add point symbols sized by literacy
  geom_sf_text(aes(label = ifelse(Literacy_rank > 70, "H",
                                  ifelse(Literacy_rank < 25, "L", ""))),
               size = 3, fontface = "bold") +
  labs(
    title = "Crime vs. Literacy",
    subtitle = "Crime Against Persons (color) and Literacy (H=High, L=Low)",
    caption = "Data: André-Michel Guerry (1833)"
  ) +
  theme_cheysson_map()

print(p9)

Historical Context

About Guerry’s Data

André-Michel Guerry (1802-1866) was a French lawyer and statistician who pioneered the use of statistical graphics and thematic maps. His 1833 Essai sur la statistique morale de la France was one of the first works to:

Use choropleth maps to visualize social data
Examine geographical patterns in crime, literacy, and social indicators
Apply statistical methods to moral and social questions

About the Variables

Crime_pers: Crimes against persons (per capita)
Crime_prop: Crimes against property (per capita)
Literacy: Percent of military conscripts who can read and write
Donations: Donations to the poor (per capita)
Infants: Population per illegitimate birth
Suicides: Annual suicides (per capita)

The Connection to Cheysson

Émile Cheysson (1836-1910), working 40-50 years after Guerry, brought similar statistical visualization techniques to new heights in the Albums de Statistique Graphique. By combining Guerry’s data with Cheysson’s visual style, we honor both pioneers of data visualization.

Available Palettes

The ggCheysson package includes multiple palettes suitable for choropleth maps:

# Sequential palettes (good for continuous rankings)
list_cheysson_pals("sequential")
#>      name       type album plate n_colors
#> 1 1881_12 sequential  1881    12        1
#> 2 1886_26 sequential  1886    26        2
#> 3 1888_27 sequential  1888    27        1
#> 4 1891_19 sequential  1891    19        1
#> 5 1891_25 sequential  1891    25        2
#> 6 1895_16 sequential  1895    16        3
#> 7 1900_28 sequential  1900    28        2

# Grouped palettes (good for categories)
list_cheysson_pals("grouped")
#>       name    type album plate n_colors
#> 1  1880_07 grouped  1880     7        5
#> 2  1881_14 grouped  1881    14        2
#> 3  1881_30 grouped  1881    30        5
#> 4  1882_18 grouped  1882    18        2
#> 5  1886_11 grouped  1886    11        4
#> 6  1886_17 grouped  1886    17        3
#> 7  1886_18 grouped  1886    18        3
#> 8  1886_24 grouped  1886    24        3
#> 9  1887_22 grouped  1887    22        2
#> 10 1891_14 grouped  1891    14        2

# Category palettes (good for discrete regions)
list_cheysson_pals("category")
#>      name     type album plate n_colors
#> 1 1880_21 category  1880    21        7
#> 2 1881_22 category  1881    22        4
#> 3 1883_30 category  1883    30        4
#> 4 1886_28 category  1886    28        3
#> 5 1906_06 category  1906     6        6
#> 6 1906_50 category  1906    50        4

Note: When using discrete = FALSE, even category palettes can create smooth color gradients for continuous data. The package includes: - 7 Sequential palettes (varying colors: 1-3) - 2 Diverging palettes (2-3 colors) - 5 Grouped palettes (2-5 colors) - 6 Category palettes (3-7 colors)

Summary

This vignette demonstrated:

Converting SpatialPolygonsDataFrame to sf objects for ggplot2
Joining spatial and tabular data
Creating choropleth maps with various Cheysson color palettes
Using Cheysson hatching patterns combined with colors - a signature feature of the Albums
Applying geom_sf_pattern() with pattern scales for authentic Cheysson cartography
Using theme_cheysson_map() for cartographic styling
Displaying multiple maps in faceted layouts
Combining historical data with historical cartographic styles

Key Cheysson Techniques Illustrated

The pattern-enhanced maps (literacy, donations, and regions) showcase Cheysson’s most distinctive cartographic innovation: combining colors with hatching patterns. This dual encoding:

Enhances visual distinction between categories
Creates rich, textured maps characteristic of 19th century statistical graphics
Improves readability and aesthetic appeal
Provides redundant encoding (both color and pattern) for better accessibility

These techniques defined the visual language of the Albums de Statistique Graphique and influenced statistical cartography for decades.

The combination of Guerry’s groundbreaking statistical data with Cheysson’s elegant visual style creates a fitting tribute to the pioneers of statistical graphics and thematic cartography.