Skip to contents

tidysoiltexture

Tidy tools for soil texture classification and ternary visualisation. Pipe-compatible functions that accept and return tibbles, with tidy evaluation support for sand/silt/clay column arguments. Classifies samples to 12 USDA texture classes and plots publication-ready texture triangles via ggplot2.

Installation 

# Install from GitHub:
pak::pak("Taakefyrsten/tidysoiltexture")

Quick start 

library(tidysoiltexture)
library(tibble)

soils <- tribble(
  ~id,   ~sand, ~silt, ~clay,
  "A",      65,    20,    15,
  "B",      20,    50,    30,
  "C",       8,    72,    20,
  "D",      40,    38,    22
)

# Classify texture
classify_texture(soils, sand, silt, clay)
#> # A tibble: 4 × 6
#>   id     sand  silt  clay .texture_class .texture_abbr
#>   <chr> <dbl> <dbl> <dbl> <chr>          <chr>
#> 1 A        65    20    15  sandy loam     SaLo
#> 2 B        20    50    30  clay loam      ClLo
#> 3 C         8    72    20  silty loam     SiLo
#> 4 D        40    38    22  loam           Lo

# Plot the texture triangle
gg_texture_triangle(soils, sand, silt, clay, colour = id)

Spatial workflows

classify_texture() dispatches automatically on sf point objects and terra SpatRaster stacks — no extra code needed:

# sf point object
library(sf)
pts_sf <- st_as_sf(soils_with_coords, coords = c("lon", "lat"), crs = 4326)
classify_texture(pts_sf, sand = sand, silt = silt, clay = clay)

# terra SpatRaster (e.g. from SoilGrids — divide by 10 to convert g/kg to %)
library(terra)
r <- rast("soilgrids_sand_silt_clay.tif")
r_class <- classify_texture(r / 10, sand = "sand", silt = "silt", clay = "clay")

Performance

Task Time
Classify 10 000 samples < 0.015 s
Classify 1 000 000 raster cells < 5 s

The vectorised backend iterates over 12 USDA polygon classes, testing all N points per class simultaneously — no row-level R loops.

Learn more

Full documentation: https://taakefyrsten.github.io/tidysoiltexture