Format: Practical workshop (module-based)
Host: Iranian Soil & Water Research Institute
Instructor/materials: Ruhollah Taghizadeh (scripts + exercises + lecture PDFs)
GitHub materials: https://github.com/RuhollahTaghizadeh/Workshop_SpatialDataAnalysisR

Overview

This workshop provides a practical introduction to spatial data analysis in R, designed for students and professionals working with environmental and geoscientific datasets. It starts with essential R skills and modern “tidy” workflows, then builds toward effective data visualization and hands-on work with spatial vector/raster data. The workshop concludes with an applied introduction to machine learning concepts and workflows relevant to spatial and environmental modelling.

All materials are organized as reproducible modules with code, exercises, and supporting PDFs.

What participants learn

By the end of the workshop, participants will be able to:

• use R confidently for data exploration and analysis
• organize projects using tidy, reproducible workflows
• create clear, publication-ready visualizations
• read, inspect, clean, and manipulate spatial data (vector and raster)
• perform common spatial operations and extract information from spatial layers
• understand and apply an introductory machine learning workflow:
  • problem formulation
  • feature preparation
  • model training and validation
  • interpretation of results (intro level)

Workshop structure (modules)

The repository is organized into short modules:

1. Basic R (core syntax, data structures, project habits)
2. Tidy workflows (data wrangling and clean analysis pipelines)
3. Visualization (effective plots and communication of results)
4. Spatial Data (working with spatial formats and spatial operations in R)
5. Machine Learning (intro) (applied ML workflow for spatial/environmental data)

Supporting lecture files include ML_01.pdf and ML_02.pdf.

Tools & topics

• R programming for analysis and reproducibility
• tidy data principles and workflow organization
• data visualization for exploration and reporting
• spatial vector/raster handling and basic geoprocessing
• introductory machine learning concepts for spatial applications

All workshop materials are openly available on GitHub at the link above.