Course introduction
Description
This course is a hands-on introduction to spatial data analysis and visualization in R. Through a combination of theory, practical exercises, and applied challenges, you will learn to work with both vector and raster data, understand coordinate reference systems (CRS), perform spatial operations, and create both static and interactive maps. The course uses key packages from the R ecosystem such as sf, terra, ggplot2, and mapview. It is structured into theoretical lessons with quizzes, practical lessons, and exercises for students to apply what they’ve learned.
You will start by understanding what spatial data is and how it differs from non-spatial data. Throughout the course, you will develop skills to transform geometries, analyze raster layers, calculate indices such as NDVI, and represent geographic phenomena on maps. By the end, you will be able to carry out your own spatial data analyses and share results through dynamic web maps.
Prerequisites
Basic knowledge of R
Familiarity with
tidyversefunctions, including basic plots withggplot2and the pipeline is recommended.
Course contents
In this section, you can see a non-exhaustive summary of what you will cover in this course.
Introduction to Spatial Data Analysis and GIS in R
- Spatial vs Non-spatial data
- Geometries
- Simple features
- Vector formats
- Downloading spatial data
- Exploratory analysis
- Import/Export
- Properties
- CHALLENGE 01 - proposed exercises
- Importance of CRS
- CRS, coordinates, georeferencing
- Geographic vs Projected CRS
- Projections
- EPSG codes, proj4, WKT...
- Exploring CRS
- CRS transformation
- CRS assignment
- On-the-fly transformations
- Web maps
- CHALLENGE 02 - proposed exercises
- Spatial predicates
- Geometry measurements
- Unary transformations
- Binary transformations
- Other operations
- Predicate functions
- Spatial filters
- Spatial joins
- Spatial measurements
- Transformations (centroid, buffer..)
- CHALLENGE 03 - proposed exercises
- Definition of raster data
- Types of resolution
- Brief introduction to remote sensing
- Common raster operations
- Vegetation indices
- Raster data exploration
- Download Digital Elevation Model (DEM)
- DEM derivatives
- Crop, reclassify...
- Arithmetic operations
- Vegetation index calculation
- RGB and false color compositions
- CHALLENGE 04 - proposed exercises
- Map 01 - Population of Spain by municipality
- Map 02a - Brown bear in Picos de Europa I
- Map 02b - Brown bear in Picos de Europa II
- Map 03 - Rivers of Galicia
- Map 04 - Andean bear in Peru
- Map 05 - Wildfire severity in Tenerife (2023)
What’s inside the course
150 lessons
13 hours of video
All the course materials
Theoretical classes, practical sessions, quizzes, and proposed exercises
Additional bibliography
Quick answer to any student’s question
What will you learn
You will learn to analyze spatial data in R, with RStudio becoming your new Geographic Information System (GIS). Specifically, you will learn to:
Use the most important packages for GIS in R
Analyze vector and raster data
Download spatial data in R
Perform common operations on vector and raster data
Georeference data
Transform coordinate reference systems (CRS)
Create maps and web maps like the one below:
Student testimonials
Here you can find all the testimonials left by students of this course (both positive and negative).
This is by far the best online course I have ever taken. Adrian Cidre is a wonderful instructor, extremely knowledgeable in the use of R as a geographical information system and--even more important--able to transfer that knowledge to students. I posted some questions while taking the course and he answered them very quickly and always to my satisfaction. I highly recommend this course. In fact, I've enrolled in his Udemy course, Mastering R: Best Practices and Essential Tools, despite the fact I'm pretty confident using R. But he's so good with R, I'm sure I will learn new things!
Excellent course. I have learned and clarified a lot of things. I am looking forward to the next one.
So far, the course is well paced and I can easily follow the examples. Everything is explained well. It's perfect for a beginner in spatial statistics. You should be familiar with basic data manipulation techniques and the tidyverse.