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Visualization

Gilles Colling

2026-10-23

Base R plot(), hexify_heatmap() for ggplot2, and building custom maps from scratch.

Installation

# Install from CRAN
install.packages("hexify")

# Or install development version from GitHub
# install.packages("pak")
pak::pak("gcol33/hexify")

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")

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")

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)")

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")

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), cex = 1, cex.main = 1)

plot(result, show_points = TRUE, point_size = "small",
     point_color = "red", main = "small (~5%)")
plot(result, show_points = TRUE, point_size = "normal",
     point_color = "red", main = "normal (~10%)")
plot(result, show_points = TRUE, point_size = "large",
     point_color = "red", main = "large (~20%)")
plot(result, show_points = TRUE, point_size = "very large",
     point_color = "red", main = "very large (~35%)")


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")

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)")

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")

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(base_size = FIG_BASE_SIZE) +
  theme(
    plot.title = element_text(face = "bold", size = 14),
    panel.grid = element_blank()
  )

Adding Custom Layers

# The map is in longitude/latitude, so the cities go on as plain points
hexify_heatmap(result, basemap = "world", title = "Cities with Labels",
               xlim = c(-5, 25), ylim = c(45, 55)) +
  geom_point(data = cities, aes(x = lon, y = lat), color = "red", size = 2) +
  geom_label(data = cities, aes(x = lon, y = lat, label = name),
             nudge_y = 0.8, size = 5)

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"
)

Customizing Colors

hexify_heatmap(
  obs_hex,
  value = "count",
  colors = "YlOrRd",
  title = "Yellow-Orange-Red Palette"
)

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))
p2 <- hexify_heatmap(obs_hex, value = "count", colors = "YlGnBu",
                     title = "YlGnBu", xlim = c(-20, 35), ylim = c(35, 65))

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"
)

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"
)

# Without basemap
hexify_heatmap(
  obs_hex,
  value = "count",
  basemap = NULL,
  xlim = c(-20, 35),
  ylim = c(35, 65),
  title = "No Basemap"
)

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)

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

Cell Shapes

cell_to_sf(), plot(), hexify_heatmap() and st_as_sf() draw cells as hexagons by default. With shape = "gosper", each cell edge is replaced depth times by three shorter segments turned the same way, which gives Gosper islands (flowsnakes); each island keeps its cell’s area, and the islands tile the sphere. With shape = "descendants", an aperture-7 cell is drawn as the outline of its descendants depth resolutions down, which nests across resolutions.

grid <- hex_grid(resolution = 2, aperture = 7)
cells <- grid_rect(c(-15, 30, 40, 65), grid)$cell_id
ortho <- "+proj=ortho +lon_0=12 +lat_0=48"
set.seed(1)
colour <- factor(sample(8, length(cells), replace = TRUE))

shapes <- do.call(rbind, lapply(c("hexagon", "gosper", "descendants"), function(s) {
  polys <- cell_to_sf(cells, grid, shape = s, depth = 4)
  polys$colour <- colour[match(polys$cell_id, cells)]
  polys$shape <- factor(s, levels = c("hexagon", "gosper", "descendants"))
  polys
}))

ggplot(sf::st_transform(shapes, ortho)) +
  geom_sf(aes(fill = colour), color = "gray25", linewidth = 0.15,
          show.legend = FALSE) +
  facet_wrap(~shape) +
  scale_fill_brewer(palette = "Set3") +
  theme_void(base_size = FIG_BASE_SIZE)

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 <- n_cells(grid)

# 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)

ggplot() +
  geom_sf(data = hexify_world, fill = "gray95", color = "gray70", linewidth = 0.2) +
  geom_sf(data = sample_polys, fill = alpha("forestgreen", 0.5),
          color = "darkgreen", linewidth = 0.4) +
  labs(title = sprintf("Random Sample of %d Cells", N),
       subtitle = sprintf("~%.0f km2 each", summary(grid)$area_km2)) +
  theme_minimal(base_size = FIG_BASE_SIZE) +
  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)
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(base_size = FIG_BASE_SIZE) +
  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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.