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hexify

CRAN status CRAN downloads Monthly downloads R-CMD-check Codecov test coverage License: MIT

Equal-Area Hexagonal Grids for Global Spatial Analysis

Multi-resolution hexagonal grids

hexify assigns geographic coordinates to equal-area hexagonal grid cells using the ISEA (Icosahedral Snyder Equal Area) projection. Every cell has the same area regardless of latitude, eliminating the sampling bias inherent in rectangular lat-lon grids. H3 (Uber’s hierarchical hex system) is also supported for interoperability with industry platforms.

Quick Start 

library(hexify)

cities <- data.frame(
  name = c("Vienna", "Paris", "Madrid"),
  lon = c(16.37, 2.35, -3.70),
  lat = c(48.21, 48.86, 40.42)
)

# ISEA equal-area grid (default)
grid <- hex_grid(area_km2 = 10000)
result <- hexify(cities, lon = "lon", lat = "lat", grid = grid)
plot(result)

# H3 grid (Uber's system)
h3_grid <- hex_grid(resolution = 4, type = "h3")
result_h3 <- hexify(cities, lon = "lon", lat = "lat", grid = h3_grid)
plot(result_h3)

Statement of Need

Spatial binning is fundamental to ecological modeling, epidemiology, and geographic analysis. Standard approaches using rectangular lat-lon grids introduce severe area distortions: a 1° cell at the equator covers ~12,300 km², while the same cell near the poles covers a fraction of that area. This violates the equal-sampling assumption underlying most spatial statistics.

Discrete Global Grid Systems (DGGS) solve this by partitioning Earth’s surface into cells of uniform area. hexify’s primary backend is ISEA (Icosahedral Snyder Equal Area): true equal-area hexagonal grids with apertures 3, 4, 7, or mixed 4/3, implemented in C++ with no external dependencies. For interoperability with industry ecosystems (FCC, Foursquare, DuckDB), hexify also supports H3 grids via a vendored C library.

Equal-area grids are directly applicable to:

  • Species distribution modeling and biodiversity assessments
  • Epidemiological surveillance and disease mapping
  • Environmental monitoring and remote sensing aggregation
  • Any analysis requiring unbiased spatial binning

Features

Core Workflow

  • hex_grid(): Define a grid by target cell area (km²) or resolution level
  • hexify(): Assign points to grid cells (data.frame or sf input)
  • plot() / hexify_heatmap(): Visualize results with base R or ggplot2

Grid Generation

  • grid_rect(): Generate cell polygons for a bounding box
  • grid_global(): Generate a complete global grid (all cells)
  • grid_clip(): Clip grid to a polygon boundary (country, region, etc.)

Cell Operations

Interoperability

Installation 

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

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

Usage Examples

Basic Point Assignment 

library(hexify)

# Define grid: ~10,000 km² cells
grid <- hex_grid(area_km2 = 10000)
grid
#> HexGridInfo: aperture=3, resolution=5, area=12364.17 km²

# Assign coordinates to cells
coords <- data.frame(
  lon = c(-122.4, 2.35, 139.7),
  lat = c(37.8, 48.9, 35.7)
)
result <- hexify(coords, lon = "lon", lat = "lat", grid = grid)

# Access cell IDs
result@cell_id

Working with sf Objects 

library(sf)

# Any CRS works - hexify transforms automatically
points_sf <- st_as_sf(coords, coords = c("lon", "lat"), crs = 4326)
result <- hexify(points_sf, area_km2 = 10000)

# Export back to sf
result_sf <- as_sf(result)

Generating Grid Polygons 

# Grid for Europe
grid <- hex_grid(area_km2 = 50000)
europe_hexes <- grid_rect(c(-10, 35, 40, 70), grid)
plot(europe_hexes["cell_id"])

# Clip to a country boundary
library(rnaturalearth)
france <- ne_countries(country = "France", returnclass = "sf")
france_grid <- grid_clip(france, grid)

Aggregating Point Data 

# Species occurrence data
occurrences <- data.frame(
  species = sample(c("Sp A", "Sp B", "Sp C"), 1000, replace = TRUE),
  lon = runif(1000, -10, 30),
  lat = runif(1000, 35, 60)
)

# Assign to grid
grid <- hex_grid(area_km2 = 20000)
occ_hex <- hexify(occurrences, lon = "lon", lat = "lat", grid = grid)

# Count per cell
occ_df <- as.data.frame(occ_hex)
occ_df$cell_id <- occ_hex@cell_id

cell_counts <- aggregate(species ~ cell_id, data = occ_df, FUN = length)
names(cell_counts)[2] <- "n_records"

# Richness per cell
richness <- aggregate(species ~ cell_id, data = occ_df,
                      FUN = function(x) length(unique(x)))
names(richness)[2] <- "n_species"

Visualization 

# Quick plot
plot(result)

# Heatmap with basemap
hexify_heatmap(occ_hex, value = "n_records", basemap = TRUE)

# Custom ggplot
library(ggplot2)
cell_polys <- cell_to_sf(cell_counts$cell_id, grid)
cell_polys <- merge(cell_polys, cell_counts, by = "cell_id")

ggplot(cell_polys) +
  geom_sf(aes(fill = n_records), color = "white", linewidth = 0.2) +
  scale_fill_viridis_c() +
  theme_minimal()

Documentation

Support

“Software is like sex: it’s better when it’s free.” — Linus Torvalds

I’m a PhD student who builds R packages in my free time because I believe good tools should be free and open. I started these projects for my own work and figured others might find them useful too.

If this package saved you some time, buying me a coffee is a nice way to say thanks. It helps with my coffee addiction.

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Citation 

@software{hexify,
  author = {Colling, Gilles},
  title = {hexify: Equal-Area Hexagonal Grids for Spatial Analysis},
  year = {2025},
  url = {https://CRAN.R-project.org/package=hexify},
  doi = {10.32614/CRAN.package.hexify}
}

License

MIT (see LICENSE.md)