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Visualization

Gilles Colling

2026-01-14

This vignette covers hexify’s visualization functions in detail: customizing appearance, showing points, creating heatmaps, and working with ggplot2.

Sample Data

We’ll use European cities throughout:

cities <- data.frame(
  name = c("Vienna", "Paris", "Madrid", "Berlin", "Rome",
           "Warsaw", "Prague", "Brussels", "Amsterdam", "Lisbon"),
  lon = c(16.37, 2.35, -3.70, 13.40, 12.50,
          21.01, 14.42, 4.35, 4.90, -9.14),
  lat = c(48.21, 48.86, 40.42, 52.52, 41.90,
          52.23, 50.08, 50.85, 52.37, 38.72)
)

grid <- hex_grid(area_km2 = 10000)
result <- hexify(cities, lon = "lon", lat = "lat", grid = grid)

Base R Plotting with plot()

The plot() method provides quick visualization with sensible defaults.

Basic Plot

plot(result, main = "European Cities")

#> Spherical geometry (s2) switched on

Customizing Colors and Styling

plot(result,
     grid_fill = "steelblue",
     grid_border = "darkblue",
     grid_alpha = 0.6,
     basemap_fill = "ivory",
     basemap_border = "gray50",
     main = "Custom Styling")

#> Spherical geometry (s2) switched on

Disabling the Basemap

plot(result,
     basemap = FALSE,
     grid_fill = "forestgreen",
     grid_border = "darkgreen",
     main = "No Basemap")

Setting Map Extent

To control the map extent, use crop = TRUE with crop_expand to add padding:

plot(result,
     crop = TRUE,
     crop_expand = 0.2,
     main = "Custom Extent (20% padding)")

#> Spherical geometry (s2) switched on

Showing Points

Points can be displayed on top of hexagon cells. By default, points are jittered within their assigned cell to avoid overplotting.

Basic Points

plot(result,
     show_points = TRUE,
     point_color = "red",
     main = "Cities with Points")

#> Spherical geometry (s2) switched on

Point Size Presets

The point_size parameter accepts presets that define what fraction of a hex cell a single point covers:

Preset Coverage
"tiny" ~2% of cell
"small" ~5% of cell
"normal" / "auto" ~10% of cell
"large" ~20% of cell
"very large" ~35% of cell 
oldpar <- par(mfrow = c(2, 2))

plot(result, show_points = TRUE, point_size = "small",
     point_color = "red", main = "small (~5%)")
#> Spherical geometry (s2) switched on
plot(result, show_points = TRUE, point_size = "normal",
     point_color = "red", main = "normal (~10%)")
#> Spherical geometry (s2) switched on
plot(result, show_points = TRUE, point_size = "large",
     point_color = "red", main = "large (~20%)")
#> Spherical geometry (s2) switched on
plot(result, show_points = TRUE, point_size = "very large",
     point_color = "red", main = "very large (~35%)")

#> Spherical geometry (s2) switched on

par(oldpar)

Custom Point Styling

plot(result,
     show_points = TRUE,
     point_size = "small",
     point_color = "darkblue",
     point_alpha = 0.8,
     grid_fill = "lightyellow",
     grid_border = "orange",
     main = "Custom Point Styling")
#> Warning in plot.window(...): "point_alpha" is not a graphical parameter
#> Warning in plot.xy(xy, type, ...): "point_alpha" is not a graphical parameter
#> Warning in axis(side = side, at = at, labels = labels, ...): "point_alpha" is
#> not a graphical parameter
#> Warning in axis(side = side, at = at, labels = labels, ...): "point_alpha" is
#> not a graphical parameter
#> Warning in box(...): "point_alpha" is not a graphical parameter
#> Warning in title(...): "point_alpha" is not a graphical parameter

#> Spherical geometry (s2) switched on

Disabling Jitter

For exact point locations (no randomization):

plot(result,
     show_points = TRUE,
     jitter = FALSE,
     point_color = "red",
     main = "Points at Cell Centers (No Jitter)")
#> Warning in plot.window(...): "jitter" is not a graphical parameter
#> Warning in plot.xy(xy, type, ...): "jitter" is not a graphical parameter
#> Warning in axis(side = side, at = at, labels = labels, ...): "jitter" is not a
#> graphical parameter
#> Warning in axis(side = side, at = at, labels = labels, ...): "jitter" is not a
#> graphical parameter
#> Warning in box(...): "jitter" is not a graphical parameter
#> Warning in title(...): "jitter" is not a graphical parameter

#> Spherical geometry (s2) switched on

ggplot2 with hexify_heatmap()

For ggplot2 users, hexify_heatmap() returns a ggplot object that can be further customized.

Basic ggplot

hexify_heatmap(result, basemap = "world", title = "European Cities")
#> Spherical geometry (s2) switched off
#> Spherical geometry (s2) switched on

Customizing with ggplot2

Since hexify_heatmap() returns a ggplot object, you can add layers and modify themes:

hexify_heatmap(result, basemap = "world") +
  labs(
    title = "Major European Cities",
    subtitle = "Assigned to ISEA hexagonal grid cells",
    caption = "Data: Sample cities"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold", size = 14),
    panel.grid = element_blank()
  )
#> Spherical geometry (s2) switched off
#> Spherical geometry (s2) switched on

Adding Custom Layers

# Get the city coordinates
city_points <- st_as_sf(cities, coords = c("lon", "lat"), crs = 4326)

hexify_heatmap(result, basemap = "world", title = "Cities with Labels") +
  geom_sf(data = city_points, color = "red", size = 2) +
  geom_sf_text(data = city_points, aes(label = name),
               nudge_y = 0.8, size = 3, color = "darkgray") +
  coord_sf(xlim = c(-5, 25), ylim = c(45, 55))
#> Spherical geometry (s2) switched off
#> Spherical geometry (s2) switched on
#> Coordinate system already present.
#> ℹ Adding new coordinate system, which will replace the existing one.
#> Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
#> give correct results for longitude/latitude data

Heatmaps with Value Mapping

For choropleth-style visualizations with aggregated data, pass a value column to hexify_heatmap().

Creating Aggregated Data

# Simulate observation data with counts
set.seed(42)
n_obs <- 100
obs_data <- data.frame(
  lon = c(rnorm(60, 10, 8), rnorm(40, 0, 10)),
  lat = c(rnorm(60, 48, 5), rnorm(40, 52, 6)),
  count = rpois(n_obs, lambda = 50)
)

# Hexify
grid <- hex_grid(area_km2 = 10000)
obs_hex <- hexify(obs_data, lon = "lon", lat = "lat", grid = grid)

Basic Heatmap

hexify_heatmap(
  obs_hex,
  value = "count",
  title = "Observation Counts"
)
#> Spherical geometry (s2) switched off
#> Spherical geometry (s2) switched on

Customizing Colors

hexify_heatmap(
  obs_hex,
  value = "count",
  colors = "YlOrRd",
  title = "Yellow-Orange-Red Palette"
)
#> Spherical geometry (s2) switched off
#> Spherical geometry (s2) switched on

Available color palettes include any from scale_fill_viridis_c() or scale_fill_distiller():

p1 <- hexify_heatmap(obs_hex, value = "count", colors = "viridis",
                     title = "viridis", xlim = c(-20, 35), ylim = c(35, 65))
#> Spherical geometry (s2) switched off
#> Spherical geometry (s2) switched on
p2 <- hexify_heatmap(obs_hex, value = "count", colors = "YlGnBu",
                     title = "YlGnBu", xlim = c(-20, 35), ylim = c(35, 65))
#> Spherical geometry (s2) switched off
#> Spherical geometry (s2) switched on

gridExtra::grid.arrange(p1, p2, ncol = 2)

Setting Map Extent

hexify_heatmap(
  obs_hex,
  value = "count",
  xlim = c(-20, 35),
  ylim = c(35, 65),
  title = "Zoomed to Region",
  legend_title = "Count"
)
#> Spherical geometry (s2) switched off
#> Spherical geometry (s2) switched on

Basemap Options

# With world basemap (default)
hexify_heatmap(
  obs_hex,
  value = "count",
  basemap = "world",
  xlim = c(-20, 35),
  ylim = c(35, 65),
  title = "With World Basemap"
)
#> Spherical geometry (s2) switched off
#> Spherical geometry (s2) switched on

# Without basemap
hexify_heatmap(
  obs_hex,
  value = "count",
  basemap = NULL,
  xlim = c(-20, 35),
  ylim = c(35, 65),
  title = "No Basemap"
)
#> Spherical geometry (s2) switched off
#> Spherical geometry (s2) switched on

World Map Helper

plot_world() provides a quick way to draw a world basemap:

plot_world(fill = "lightgray", border = "gray50")

Customizing the World Map

plot_world(
  fill = "antiquewhite",
  border = "sienna",
  xlim = c(-30, 50),
  ylim = c(30, 70)
)

Pentagon Cell Visualization

The ISEA grid contains 12 pentagonal cells at the icosahedron vertices. Here’s how to visualize them:

# Pentagon locations (icosahedron vertices in standard ISEA orientation)
pentagon_coords <- data.frame(
  type = c("Pole", "Pole", rep("Vertex", 10)),
  lon = c(0, 0, seq(0, 324, by = 36)),
  lat = c(90, -90, rep(c(26.57, -26.57), 5))
)

# Assign to grid and get polygons
grid <- hex_grid(area_km2 = 500000)
pentagon_cells <- lonlat_to_cell(pentagon_coords$lon, pentagon_coords$lat, grid)

pentagon_polys <- cell_to_sf(pentagon_cells, grid)
pentagon_polys_wrapped <- st_wrap_dateline(
  pentagon_polys,
  options = c("WRAPDATELINE=YES", "DATELINEOFFSET=180"),
  quiet = TRUE
)

ggplot() +
  geom_sf(data = hexify_world, fill = "gray95", color = "gray70", linewidth = 0.2) +
  geom_sf(data = pentagon_polys_wrapped, fill = alpha("purple", 0.6),
          color = "purple", linewidth = 0.8) +
  labs(
    title = "Pentagon Cell Locations",
    subtitle = "12 pentagonal cells at icosahedron vertices (area = 5/6 of hexagons)"
  ) +
  theme_minimal() +
  theme(axis.text = element_blank(), axis.ticks = element_blank())

Random Sampling Visualization

Visualizing uniformly sampled cells across Earth:

# Grid parameters (coarse for faster build)
grid <- hex_grid(area_km2 = 200000, aperture = 3)
max_cell <- 10 * (3^grid@resolution) + 2

# Sample random cell IDs
set.seed(123)
N <- 50
random_cells <- sample(1:max_cell, N, replace = FALSE)

# Generate polygons for sampled cells
sample_polys <- cell_to_sf(random_cells, grid)
sample_polys_wrapped <- st_wrap_dateline(
  sample_polys,
  options = c("WRAPDATELINE=YES", "DATELINEOFFSET=180"),
  quiet = TRUE
)

ggplot() +
  geom_sf(data = hexify_world, fill = "gray95", color = "gray70", linewidth = 0.2) +
  geom_sf(data = sample_polys_wrapped, fill = alpha("forestgreen", 0.5),
          color = "darkgreen", linewidth = 0.4) +
  labs(title = sprintf("Random Sample of %d Cells (~%.0f km2 each)", N, grid@area_km2)) +
  theme_minimal() +
  theme(axis.text = element_blank(), axis.ticks = element_blank())

Building Custom Visualizations

For full control, generate polygons directly and use ggplot2:

# Create data with a numeric variable
set.seed(456)
stations <- data.frame(
  lon = runif(50, -10, 30),
  lat = runif(50, 35, 60),
  temperature = rnorm(50, mean = 15, sd = 5)
)

# Hexify (coarser grid for faster build)
grid <- hex_grid(area_km2 = 20000)
stations_hex <- hexify(stations, lon = "lon", lat = "lat", grid = grid)

# Aggregate temperature by cell
stations_df <- as.data.frame(stations_hex)
stations_df$cell_id <- stations_hex@cell_id
cell_temps <- aggregate(temperature ~ cell_id, data = stations_df, FUN = mean)

# Generate polygons and merge data
cell_polys <- cell_to_sf(cell_temps$cell_id, grid)
cell_polys <- merge(cell_polys, cell_temps, by = "cell_id")

# Custom visualization
europe <- hexify_world[hexify_world$continent == "Europe", ]

ggplot() +
  geom_sf(data = europe, fill = "gray95", color = "gray60", linewidth = 0.2) +
  geom_sf(data = cell_polys, aes(fill = temperature),
          color = "white", linewidth = 0.2) +
  scale_fill_gradient2(
    low = "blue", mid = "white", high = "red",
    midpoint = 15, name = "Temp (°C)"
  ) +
  coord_sf(xlim = c(-10, 30), ylim = c(35, 60)) +
  labs(
    title = "Mean Temperature by Grid Cell",
    subtitle = "Diverging color scale centered at 15°C"
  ) +
  theme_minimal() +
  theme(
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    panel.grid = element_line(color = "gray90")
  )

Function Reference

Function Description
plot() Base R plot method for HexData objects
hexify_heatmap() ggplot2 visualization, returns modifiable ggplot object
plot_world() Quick world basemap
cell_to_sf() Generate sf polygons from cell IDs

See Also

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