Practical 1: Getting started with R and notebooks

This first practical introduces the core environment used throughout the workshop:
R programming within an interactive notebook (Google Colab).

In order to work with underlying spatial datasets it is important to understand how to:

• Run code step-by-step
  
• Create and store data objects
  
• Load and inspect datasets
  
• Perform simple data operations

This practical provides the foundation for all subsequent exercises.

Learning objectives

After completing this practical you should be able to:

• Understand what R is and how it is used
  
• Navigate and run a notebook environment
  
• Create and manipulate basic data objects
  
• Import simple datasets
  
• Perform basic data operations using dplyr

This practical introduces three key ideas:

1. Working in notebooks

You will learn how notebooks combine:

• Text (documentation and explanation)
• Code (analysis steps)
• Outputs (results and visualisations)

This allows you to build transparent and reproducible workflows.

2. Creating and working with data

You will learn how to:

• Create objects in R
• Store datasets
• Inspect their structure
• Understand variables and values

3. Basic data manipulation

You will use simple functions from dplyr to:

• Filter data
• Select variables
• Perform basic transformations

These are the building blocks of all data analysis workflows.

Running the practical

This practical is implemented as an interactive notebook. Open the notebook in Google Colab:

Launch notebook

Next steps

In this exercise, you learn how to load and manipulate data. In the next practical, you will build on this by:

• Cleaning and structuring datasets
  
• Creating visualisations
  
• Exploring patterns in data