Data Analysis in Natural Sciences

An R-Based Approach

Author

Affiliation

Jimmy Moses

Papua New Guinea University of Technology

Published

May 2026

Welcome

Welcome to Data Analysis in Natural Sciences: An R-Based Approach, a comprehensive, practical guide designed for students, professionals, and researchers across the natural sciences. This book provides hands-on methods for analyzing and visualizing data using R, with real-world applications spanning ecology, forestry, agriculture, marine biology, environmental science, and beyond.

📘 About This Book

This is an open-access online textbook that teaches modern data analysis techniques using the R programming language. Whether you’re a beginner learning R for the first time or an experienced researcher looking to adopt tidyverse workflows, this book offers practical guidance with reproducible code examples.

What You’ll Learn

📊 Data Analysis Fundamentals

Import, clean, and transform data
Exploratory data analysis techniques
Working with the tidyverse ecosystem

📈 Statistical Methods

Hypothesis testing frameworks
Parametric and non-parametric tests
Regression analysis with tidymodels

🎨 Data Visualization

Publication-quality graphics with ggplot2
Interactive visualizations
Effective scientific communication

🌍 Real-World Applications

Conservation case studies
Environmental data analysis
Reproducible research practices

Book Structure

Part	Chapters	Topics
Getting Started	1-2	Introduction to R, data structures, importing data
Data Analysis Fundamentals	3-5	EDA, hypothesis testing, statistical tests
Data Visualization	6-7	ggplot2, advanced graphics, interactive plots
Advanced Topics	8-10	Regression analysis, advanced modeling, conservation applications
R in Context	11	Integrations with jamovi, JASP, Positron, Jupyter, reticulate, plumber

Who Is This Book For?

This book is designed for anyone working with data in the natural sciences:

🎓 Students: Undergraduate and postgraduate students in biology, ecology, forestry, agriculture, and environmental sciences
🔬 Researchers: Scientists seeking to enhance their data analysis and visualization skills
🌿 Practitioners: Conservation professionals, environmental consultants, and natural resource managers
📊 Data Enthusiasts: Anyone interested in learning R for scientific data analysis

Getting Started

New to R? Start with Chapter 1: Introduction to Data Analysis for installation instructions and your first steps with R and RStudio.

Features

✅ Complete code examples: All code is fully reproducible ✅ Real datasets: Learn with actual data from ecological and environmental research ✅ Modern R practices: Tidyverse and tidymodels workflows throughout ✅ Professional tips: Best practices from experienced researchers ✅ Exercises: Practice problems to reinforce learning ✅ Open access: Free to read online

About the Author

Jimmy Moses is a Papua New Guinean entomologist and lecturer at the Papua New Guinea University of Technology’s School of Forestry. He specializes in ant ecology, biostatistics, and geospatial analysis, with a Ph.D. in Entomology from the University of South Bohemia (2021).

His research combines ecological field studies with modern analytical approaches, resulting in publications in journals including Global Ecology and Biogeography and Proceedings of the Royal Society B. His passion for making complex analytical methods accessible drives both his teaching and this book.

How to Use This Book

📖 Read Online

Browse chapters directly in your web browser. Use the navigation menu to move between sections.

💻 Run the Code

Copy code examples into R or RStudio. All code is designed to be reproducible with the included datasets.

🔧 Adapt & Apply

Modify examples for your own data and research questions. The techniques are broadly applicable.

Prerequisites

To get the most out of this book, you should have:

Basic computer skills
R and RStudio installed (instructions in Chapter 1)
Curiosity about data and natural sciences!

No prior programming experience is required. I start from the basics and build up progressively.

A note on the example datasets

The book ships with ten dataset directories named after scientific disciplines (forestry/, epidemiology/, marine/, and so on). Most of these CSV files came from public TidyTuesday-style sources and kept their original schemas. The directory names reflect the chapter context in which each file is used, not always what the file literally contains. For example, forestry/forest_inventory.csv is actually a Star Wars character table that we treat as a stand-in for continuous-variable analysis.

This is documented in full at data/MISMATCHES.md. When you reach a chapter exercise that asks you to load one of these files, glance at names(df) and the first few rows before assuming the variables match the directory name. If you have a real domain dataset you’d like to swap in, the code in each chapter is small enough to adapt.

Get Involved

🐛 Found an Issue?

Report errors or suggest improvements on GitHub Issues

🤝 Want to Contribute?

Contributions welcome! See our Contributing Guide

Acknowledgments

This book would not be possible without:

The R Core Team and the incredible R community
The tidyverse and tidymodels teams for transforming how I work with data
RStudio/Posit for excellent development tools
The Quarto team for this beautiful publishing system
All the data providers whose open datasets make the examples possible
Students and colleagues who provided feedback and inspiration

Ready to start your data analysis journey?

Begin with Chapter 1 →

--- pagetitle: "Data Analysis in Natural Sciences" prefer-html: true --- ![](images/dans_coverpage_final.png){fig-align="center" width="100%"} ::: {.content-block} ## Welcome {.unnumbered} Welcome to **Data Analysis in Natural Sciences: An R-Based Approach**, a comprehensive, practical guide designed for students, professionals, and researchers across the natural sciences. This book provides hands-on methods for analyzing and visualizing data using R, with real-world applications spanning ecology, forestry, agriculture, marine biology, environmental science, and beyond. ::: {.callout-note appearance="simple" icon=false} ## 📘 About This Book This is an **open-access online textbook** that teaches modern data analysis techniques using the R programming language. Whether you're a beginner learning R for the first time or an experienced researcher looking to adopt tidyverse workflows, this book offers practical guidance with reproducible code examples. ::: ## What You'll Learn {.unnumbered} ::: {.grid} ::: {.g-col-12 .g-col-md-6} ### 📊 Data Analysis Fundamentals - Import, clean, and transform data - Exploratory data analysis techniques - Working with the tidyverse ecosystem ::: ::: {.g-col-12 .g-col-md-6} ### 📈 Statistical Methods - Hypothesis testing frameworks - Parametric and non-parametric tests - Regression analysis with tidymodels ::: ::: {.g-col-12 .g-col-md-6} ### 🎨 Data Visualization - Publication-quality graphics with ggplot2 - Interactive visualizations - Effective scientific communication ::: ::: {.g-col-12 .g-col-md-6} ### 🌍 Real-World Applications - Conservation case studies - Environmental data analysis - Reproducible research practices ::: ::: ## Book Structure {.unnumbered} | Part | Chapters | Topics | |------|----------|--------| | **Getting Started** | 1-2 | Introduction to R, data structures, importing data | | **Data Analysis Fundamentals** | 3-5 | EDA, hypothesis testing, statistical tests | | **Data Visualization** | 6-7 | ggplot2, advanced graphics, interactive plots | | **Advanced Topics** | 8-10 | Regression analysis, advanced modeling, conservation applications | | **R in Context** | 11 | Integrations with jamovi, JASP, Positron, Jupyter, reticulate, plumber | ## Who Is This Book For? {.unnumbered} This book is designed for anyone working with data in the natural sciences: - 🎓 **Students**: Undergraduate and postgraduate students in biology, ecology, forestry, agriculture, and environmental sciences - 🔬 **Researchers**: Scientists seeking to enhance their data analysis and visualization skills - 🌿 **Practitioners**: Conservation professionals, environmental consultants, and natural resource managers - 📊 **Data Enthusiasts**: Anyone interested in learning R for scientific data analysis ::: {.callout-tip} ## Getting Started New to R? Start with [Chapter 1: Introduction to Data Analysis](chapters/01-introduction.qmd) for installation instructions and your first steps with R and RStudio. ::: ## Features {.unnumbered} ✅ **Complete code examples**: All code is fully reproducible ✅ **Real datasets**: Learn with actual data from ecological and environmental research ✅ **Modern R practices**: Tidyverse and tidymodels workflows throughout ✅ **Professional tips**: Best practices from experienced researchers ✅ **Exercises**: Practice problems to reinforce learning ✅ **Open access**: Free to read online ## About the Author {.unnumbered} **Jimmy Moses** is a Papua New Guinean entomologist and lecturer at the Papua New Guinea University of Technology's School of Forestry. He specializes in ant ecology, biostatistics, and geospatial analysis, with a Ph.D. in Entomology from the University of South Bohemia (2021). His research combines ecological field studies with modern analytical approaches, resulting in publications in journals including *Global Ecology and Biogeography* and *Proceedings of the Royal Society B*. His passion for making complex analytical methods accessible drives both his teaching and this book. ## How to Use This Book {.unnumbered} ::: {.grid} ::: {.g-col-12 .g-col-md-4} ### 📖 Read Online Browse chapters directly in your web browser. Use the navigation menu to move between sections. ::: ::: {.g-col-12 .g-col-md-4} ### 💻 Run the Code Copy code examples into R or RStudio. All code is designed to be reproducible with the included datasets. ::: ::: {.g-col-12 .g-col-md-4} ### 🔧 Adapt & Apply Modify examples for your own data and research questions. The techniques are broadly applicable. ::: ::: ## Prerequisites {.unnumbered} To get the most out of this book, you should have: - Basic computer skills - R and RStudio installed (instructions in Chapter 1) - Curiosity about data and natural sciences! *No prior programming experience is required.* I start from the basics and build up progressively. ## A note on the example datasets {.unnumbered} The book ships with ten dataset directories named after scientific disciplines (`forestry/`, `epidemiology/`, `marine/`, and so on). Most of these CSV files came from public TidyTuesday-style sources and kept their original schemas. The directory names reflect the chapter context in which each file is used, not always what the file literally contains. For example, `forestry/forest_inventory.csv` is actually a Star Wars character table that we treat as a stand-in for continuous-variable analysis. This is documented in full at [`data/MISMATCHES.md`](https://github.com/jm0535/dains/blob/main/data/MISMATCHES.md). When you reach a chapter exercise that asks you to load one of these files, glance at `names(df)` and the first few rows before assuming the variables match the directory name. If you have a real domain dataset you'd like to swap in, the code in each chapter is small enough to adapt. ## Get Involved {.unnumbered} ::: {.grid} ::: {.g-col-12 .g-col-md-6} ### 🐛 Found an Issue? Report errors or suggest improvements on [GitHub Issues](https://github.com/jm0535/dains/issues) ::: ::: {.g-col-12 .g-col-md-6} ### 🤝 Want to Contribute? Contributions welcome! See our [Contributing Guide](https://github.com/jm0535/dains/blob/main/CONTRIBUTING.md) ::: ::: ## Acknowledgments {.unnumbered} This book would not be possible without: - The **R Core Team** and the incredible R community - The **tidyverse** and **tidymodels** teams for transforming how I work with data - **RStudio/Posit** for excellent development tools - The **Quarto** team for this beautiful publishing system - All the **data providers** whose open datasets make the examples possible - **Students and colleagues** who provided feedback and inspiration --- ::: {.text-center} **Ready to start your data analysis journey?** [Begin with Chapter 1 →](chapters/01-introduction.qmd){.btn .btn-primary .btn-lg} ::: :::