Aggregate Environmental Data into H3 Hexagons

Source: R/functions.R

Converts environmental point data into multi-resolution H3 hexagonal bins with aggregated statistics and geometries for mapping. Uses dbplyr lazy evaluation to defer collection until after aggregation.

prep_env_hex(df_env, res_range, env_stat)

Arguments

df_env

dbplyr lazy table with H3 index columns (hex_h3res*) and qty column

res_range

Integer vector of H3 resolution levels to generate (e.g., 3:5)

env_stat

Character string specifying aggregation function: "mean", "median", "min", "max", "sd"

Value

List of sf objects, one per resolution level, each with columns:

  • resolution - H3 resolution level

  • hexid - H3 hexagon identifier

  • env.value - aggregated environmental value

  • tooltip - rounded value for display

  • geometry - sf geometry (hexagon polygon)

Details

This function uses dbplyr lazy evaluation to efficiently aggregate data across multiple H3 resolutions via union_all. Geometries are joined from a pre-computed sf object (sf_hex).

See also

map_env for visualization

get_env for data retrieval

Examples

if (FALSE) { # \dontrun{
df_env <- get_env("t_deg_c", qtr = 1:4, date_range = c("2000-01-01", "2020-12-31"), min_depth = 0, max_depth = 100)
env_hex <- prep_env_hex(df_env, res_range = 3:5, env_stat = "mean")
} # }