The goal of ggsoiltexture is to provide a simple ggplot function for the plotting of soil textural data. The package was peer-reviewed in the journal SoftwareX. During these months, the package has been expanded to include several classifications, including the UK, Australia, and New Zealand. We hope that the package will be useful in your research.
If you use this package in your research, please cite the associated paper:
Acevedo, S., Willaredt, M., O’Brien, L., Giraldo, C., Vega, A., Contreras, C., & Pastén, P. (2025). ggsoiltexture: An R package based on ggplot2 for soil texture data visualization. SoftwareX, 31, 102293. https://doi.org/10.1016/j.softx.2025.102293
Or using BibTeX:
@article{Acevedo2025,
title = {ggsoiltexture: An R package based on ggplot2 for soil texture data visualization},
volume = {31},
ISSN = {2352-7110},
url = {http://dx.doi.org/10.1016/j.softx.2025.102293},
DOI = {10.1016/j.softx.2025.102293},
journal = {SoftwareX},
publisher = {Elsevier BV},
author = {Acevedo, Sara and Willaredt, Moreen and O’Brien, Lauren and Giraldo, Carolina and Vega, Alejandra and Contreras, Cristina and Pastén, Pablo},
year = {2025},
month = sep,
pages = {102293}
}Thanks to Moreen, Tom, and Lauren for collaborating to improve ggsoiltexture.
- Moreen Willaredt GitHub Account
- Tom Bishop GitHub Account
- Lauren O’Brien GitHub Account
The code was developed based on the ggplot_piper functions written by Jonh Dorian and inspired by the R package ggtern. Thanks for sharing your knowledge. Also, thanks to the X users that give us feedback about functionalities and new ideas.
You can install the last version of ggsoiltexture from GitHub. This package will be available in CRAN after publication in a journal.
# install.packages("devtools")
# devtools::install_github("Saryace/ggsoiltexture")You can plot a simple soil textural triangle directly:
library(tidyverse)
library(ggsoiltexture)
some_data <- tibble(id = c("Soil A","Soil B","Soil C","Soil D","Soil E"),
clay = c(10,20,25,20,50),
silt = c(35,15,45,30,10),
sand = c(55,65,30,50,40),
om = c(5,15,5,12,7))
ggsoiltexture(some_data)
Also, it is possible to remove the grid:
ggsoiltexture(some_data,
show_grid = FALSE)
Because this function is based on ggplot, you can use ggplot2 functionalities and other packages
library(ggrepel)
pub_plot <-
ggsoiltexture(some_data) +
geom_point(aes(color = om), size = 6) +
scale_color_continuous(type = "viridis") +
labs(color = "Organic\nMatter (%)") +
geom_label_repel(aes(label = id), box.padding = 0.5) +
theme(legend.title = element_text(face = "bold"),
legend.position = "bottom")
pub_plot
ggsoiltexture includes some classification systems from USDA, New Zealand, Germany and Swiss.
ggsoiltexture(some_data,
show_grid = FALSE,
class = "USDA")
ggsoiltexture(some_data,
show_grid = FALSE,
class = "GERMAN")
ggsoiltexture(some_data,
show_grid = FALSE,
class = "NZ")
ggsoiltexture(some_data,
show_grid = FALSE,
class = "SWISS")
ggsoiltexture(some_data,
show_grid = FALSE,
class = "SSEW")
The package has the polygon data for each type of classification, so they can be used separately for more advanced plotting. Due to the amount of information, it is recommended to remove all legends and add a description of the figure.
data("nz_polygons")
data("usda_polygons")
ggsoiltexture(tibble(sand = 20,
silt = 15,
clay = 65),
show_grid = FALSE,
class = NULL) +
geom_polygon(data = nz_polygons,
aes(x = x, y = y, fill = label),
alpha = .5,
color = "grey90") +
geom_text(data = nz_polygons %>%
dplyr::group_by(CODE) %>%
dplyr::summarise_if(is.numeric, mean, na.rm = TRUE),
aes(label = CODE, color = CODE),
show.legend=FALSE,
size = 3.5) +
geom_polygon(data = usda_polygons,
aes(x = x, y = y, fill = label),
alpha = 0,
linetype="dotted",
color = "grey20") +
geom_text(data = usda_polygons %>%
dplyr::group_by(label) %>%
dplyr::summarise_if(is.numeric, mean, na.rm = TRUE),
aes(label = label),
size = 3.5) +
guides(color = "none", fill = "none") +
labs(caption = "Dotted lined polygons: USDA classification\nColored polygons: New Zealand Classification")
Munsell colors are widely used for soil classification. Using the library ´munsell´ the color can be transformed to hexcolor and be plotted.
library(munsell)
library(ggrepel)
munsell_data <- tibble(sand = c(45,60,30,50,70,40,55,65,35,25,80,20),
clay = c(20,15,25,28,10,30,22,12,58,35,8,70),
silt = 100 - sand - clay,
om = c(2.3,1.8,3.2,2.0,1.5,2.6,2.8,1.2,3.0,4.0,1.0,4.2),
om_level = ifelse(om > 2, "high", "low"),
munsell = c("10YR 3/4","7.5YR 4/6","7.5YR 4/6", "10YR 4/2","5YR 3/6",
"10YR 5/8","7.5YR 5/4","2.5Y 4/2","10YR 6/6",
"5YR 4/8","7.5YR 6/2", "10YR 7/4"))
munsell_data$color_hex <- mnsl(munsell_data$munsell)
plot_munsell <-
ggsoiltexture(munsell_data) +
scale_color_identity() +
geom_point(aes(color = color_hex, size = om, group = om_level)) +
geom_label_repel(aes(label = munsell), box.padding = 0.5)
plot_munsell
Even it is possible to facet them, due to is based on ggplot :)
plot_munsell +
facet_wrap(vars(om_level))
Because of cats. This example is just for motivate to the ggsoiltexture’s users to explore any idea and try to plot it.
library(ggcats)## ~~ Package ggcats
## Visit https://r-coder.com/ for R tutorials ~~
library(gganimate)
ggsoiltexture(tibble(sand = c(90, 30, 50, 0, 50),
silt = c(5, 30, 30, 33, 10),
clay = c(5, 40, 20, 67, 40)),
class = NULL) +
geom_cat(aes(cat = sand), cat = "shironeko", size = 6) +
transition_reveal(sand)
We welcome any issues and pull requests.
Please report any bugs you find by submitting an issue on github.com/Saryace/ggsoiltexture/issues.
If you wish to contribute, please submit a pull request following the guidelines stated in CONTRIBUTING.md.