Notebook 1: R basics and climate data

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

It begins with the basics of working in R and then applies those skills to climate datasets, helping participants move from simple coding tasks into practical data analysis workflows relevant to restoration planning.

Notebook 1 is designed as the foundation for all later practical work. It introduces the logic of working step by step in a notebook, building confidence with code, data, and outputs before moving into more complex land health, soil health, and spatial workflows.

What you will learn and do

This notebook is organised around three connected parts:

1. Get started with R and notebooks

The first section introduces the basic logic of working in R within a notebook environment.

What R is and why it is useful
How notebooks combine text, code, and outputs
How to run cells, create objects, and work step by step
How libraries such as dplyr and ggplot2 extend what R can do

Working in notebooks and learning R basics

2. Work with climate datasets

The second section introduces climate as a restoration-relevant theme and applies the same workflow to real workshop datasets.

Loading rainfall and temperature data
Inspecting dataset structure and variable names
Checking values, missing data, and summary statistics
Using simple transformations and grouped summaries

Climate data inspection and summary workflows

3. Visualise climate patterns

The final section uses simple plots and outputs to help participants begin interpreting and communicating climate information.

Creating introductory plots with ggplot2
Exploring variation across locations and variables
Interpreting simple patterns in rainfall and temperature
Saving outputs to an organised folder structure

Climate data in restoration

Climate is one of the most important background conditions shaping restoration outcomes. Variables such as rainfall and temperature influence:

Vegetation growth and seasonal productivity
Soil moisture and water availability
Drought stress and regeneration potential
Erosion risk and land degradation dynamics
Suitability of different restoration interventions in different places

Figure 1. Examples of precipitation and temperature trend rasters from the GGW Dashboards.

Climate interacts with other landscape processes, including vegetation cover, land use, and soil condition. For this reason, climate data is often used together with land health and soil health indicators when identifying priority areas, interpreting trends, or designing restoration strategies.