Visualization with ggplot2

library(bayesiansurpriser)
#> bayesiansurpriser: Bayesian Surprise for De-Biasing Thematic Maps
#> Inspired by Correll & Heer (2017) - IEEE InfoVis
library(sf)
#> Warning: package 'sf' was built under R version 4.5.2
#> Linking to GEOS 3.13.0, GDAL 3.8.5, PROJ 9.5.1; sf_use_s2() is TRUE
library(ggplot2)
#> Warning: package 'ggplot2' was built under R version 4.5.2

Overview

The bayesiansurpriser package provides seamless ggplot2 integration through custom scales and computed surprise values that can be mapped to aesthetics.

Loading Example Data

nc <- st_read(system.file("shape/nc.shp", package = "sf"), quiet = TRUE)

Basic Workflow: Compute then Plot

The recommended workflow is to compute surprise first, then use ggplot2:

# Compute surprise
result <- surprise(nc, observed = SID74, expected = BIR74)

# Plot with ggplot2 using geom_sf
ggplot(result) +
  geom_sf(aes(fill = surprise)) +
  scale_fill_surprise() +
  labs(title = "Bayesian Surprise Map")

Color Scales

Sequential Scale: scale_fill_surprise()

For absolute surprise values (always positive):

ggplot(result) +
  geom_sf(aes(fill = surprise)) +
  scale_fill_surprise(option = "inferno") +
  labs(title = "Inferno Palette")

Available viridis options: “viridis”, “magma”, “plasma”, “inferno”, “cividis”, “rocket”, “mako”, “turbo”

p1 <- ggplot(result) +
  geom_sf(aes(fill = surprise)) +
  scale_fill_surprise(option = "viridis") +
  labs(title = "Viridis")

p2 <- ggplot(result) +
  geom_sf(aes(fill = surprise)) +
  scale_fill_surprise(option = "plasma") +
  labs(title = "Plasma")

p1
p2

Diverging Scale: scale_fill_surprise_diverging()

For signed surprise (positive = over-representation, negative = under-representation):

ggplot(result) +
  geom_sf(aes(fill = signed_surprise)) +
  scale_fill_surprise_diverging() +
  labs(title = "Diverging Scale for Signed Surprise")

Custom colors:

ggplot(result) +
  geom_sf(aes(fill = signed_surprise)) +
  scale_fill_surprise_diverging(
    low = "#2166AC",   # Blue
    mid = "#F7F7F7",   # Light gray
    high = "#B2182B"   # Red
  ) +
  labs(title = "Custom Diverging Colors")

Binned Scale: scale_fill_surprise_binned()

For discrete categories:

ggplot(result) +
  geom_sf(aes(fill = surprise)) +
  scale_fill_surprise_binned(n.breaks = 5) +
  labs(title = "Binned Surprise Scale")

Combining with Other ggplot2 Elements

Adding Labels

# Top 5 most surprising counties
top5 <- result[order(-result$surprise), ][1:5, ]

ggplot(result) +
  geom_sf(aes(fill = surprise)) +
  geom_sf_text(data = top5, aes(label = NAME), size = 3) +
  scale_fill_surprise() +
  labs(title = "Top 5 Most Surprising Counties Labeled")
#> Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
#> give correct results for longitude/latitude data

Faceting

# Compare two time periods
result74 <- surprise(nc, observed = SID74, expected = BIR74)
result79 <- surprise(nc, observed = SID79, expected = BIR79)

result74$period <- "1974-78"
result79$period <- "1979-84"

combined <- rbind(result74, result79)

ggplot(combined) +
  geom_sf(aes(fill = surprise)) +
  scale_fill_surprise() +
  facet_wrap(~period) +
  labs(title = "Surprise by Time Period")

Theme Customization

ggplot(result) +
  geom_sf(aes(fill = surprise)) +
  scale_fill_surprise(name = "Surprise\n(bits)") +
  labs(
    title = "Bayesian Surprise: NC SIDS Data",
    subtitle = "Identifying unexpectedly high/low SIDS rates",
    caption = "Data: NC SIDS 1974-78"
  ) +
  theme_minimal() +
  theme(
    legend.position = "bottom",
    legend.key.width = unit(2, "cm")
  )

Non-Spatial Data

For non-spatial data, use standard ggplot2 geoms after computing surprise:

# Create example data
df <- data.frame(
  region = LETTERS[1:10],
  observed = c(50, 120, 80, 200, 45, 150, 90, 180, 60, 110),
  expected = c(100, 100, 100, 100, 100, 100, 100, 100, 100, 100) * 10
)

result_df <- surprise(df, observed = observed, expected = expected)

ggplot(result_df, aes(x = reorder(region, -surprise), y = surprise)) +
  geom_col(aes(fill = surprise)) +
  scale_fill_surprise() +
  labs(x = "Region", y = "Surprise (bits)",
       title = "Surprise by Region") +
  theme_minimal()

Best Practices

1. Use diverging scales for signed surprise: Makes interpretation intuitive
2. Consider binned scales for communication: Discrete categories are easier to read
3. Label notable regions: Help viewers identify specific areas
4. Include a legend title with units: “Surprise (bits)” clarifies the measure
5. Use minimal themes for maps: Reduce visual clutter