• Lecture 1: Introduction to base R (2 hours)
– Objects in R (vectors/lists/matrices/dataframes)
– Control flow and iteration (for/while loops, if/else statements)
– Functions
• Lecture 2: Introduction to the tidyverse (2 hours)
– Tidy data
– Reshaping and joining
– Subsetting and transformation
– Plotting
• Lecture 3: Introduction to spatial data (2 hours)
– Packages installation
– Data formats and structure (raster/vector)
– Coordinate Reference Systems
• Lecture 4: Vector data manipulation in R (2 hours)
– Importing data
– Attributes and features
– Spatial operations (subset/overlap/join/aggregate/distance)
– Geometry operations (simplification/centroid/buffer/subset/transform)
– Plotting vector data
• Lecture 5: Raster data in R (2 hours)
– Raster data structure
– The terra and stars packages
– Basic raster data manipulation with both packages
– Data cubes handling with stars
• Lecture 6: Raster-vector interaction and reprojection (2 hours)
– Raster cropping and extraction
– Conversion of vector to raster and viceversa
– Reprojection and working with CRS
• Lecture 7: Geographic data sources and attribute data (2 hours)
– Retrieving data from open sources
– Adding attributes to geographic data
– Plotting attributes and features
– Writing to disk and saving maps
• Lecture 8: Manual digitization of maps with QGIS (1.5 hours)
– Loading a drawn map into QGIS
– Georeferencing maps
– Adding attributes
• Lecture 9: Least cost path calculation (1.5 hours)
– Calculation of least cost paths
• Lecture 10: Geographic data in statistical models (2 hours)
– Linear regression in base R
– Standard error correction
– Regression results into tidy data
– Using geodata in regressions
– Adjustments for spatial correlation
Teaching material
• Geocomputation with R
• Spatial data science with applications in R

Grading
There will be a take-home exam consisting of either the applied part of a small research project or the replication of either the main result or the main maps/figures of a paper (list TBD) with publicly available data. In either case students will have to utilize geographic data and provide reproducible code as well as a short document describing the steps taken and the results. Evaluation will be based mostly on coding execution and presentation of the results.