Variogram Tutorial (R)

A short, self-contained tutorial for building experimental and fitted semivariograms in R using gstat and the built-in meuse dataset (no external files).

What you'll learn

• Nugget, sill, range — what they mean and how to read them
• Empirical semivariogram computation
• Model fitting (exponential example)
• Directional variograms (anisotropy check)

Quick start

A) View-only (R script shown, but already run for you)

Open the rendered HTML tutorial:

• https://parker-group.github.io/variogram_tutorial1/
• variogram_tutorial.html (in this repo)

B) Hands-on (R installed on your computer - you run the script)

In R:

install.packages(c("gstat", "sp", "sf", "rmarkdown"))  # first time
rmarkdown::render("variogram_tutorial1.rmd", output_format = "html_document")

C) Hands-on in your browser (no install - you run R from your browser)

Click to launch an interactive RStudio session with Binder:

When RStudio opens in the browser:

1. In the Files pane, click variogram_tutorial1.rmd
2. Click Knit to render, or use Run ▶ to step through

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Reproducing directly from the README in R

If you want to quickly reproduce the example without opening the .Rmd file, copy/paste this into your R console:

# Install required packages (skip if already installed)
install.packages(c("gstat", "sp"))

# Load libraries
library(sp)
library(gstat)

# Load the example data
data(meuse)
coordinates(meuse) <- ~x+y

# Compute experimental variogram
v_exp <- variogram(log(zinc)~1, meuse)
plot(v_exp)

# Fit an exponential model
v_model <- fit.variogram(v_exp, model=vgm(psill=0.6, model="Exp", range=900, nugget=0.05))
plot(v_exp, v_model)

This minimal example runs the core variogram workflow without needing any extra files.

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Next Step

Continue to Spatial Dependence Part 2
▶️ View the live tutorial here

Notes

• Binder setup files (runtime.txt and install.R) are included for reproducibility.
• First Binder launch may take 1–3 minutes while it builds the environment.