R Programming for Agricultural Data Analysis
An undergraduate textbook and practical companion · 0+2
Preface
Agricultural data describe living systems. A yield measurement reflects a crop, its environment, a management decision, and a method of measurement. R helps us organize those observations and examine the evidence, but it cannot decide whether an experiment was well designed or whether a conclusion makes agricultural sense.
This textbook introduces R through questions that undergraduate students of agriculture can recognize: How variable is yield? Do treatments differ? How does a crop grow? Can weather records help us anticipate future conditions? Students begin with objects and simple calculations, then develop a complete workflow from data entry to a reproducible report.
This book develops my earlier lecture notes for Data Analysis Using Statistical Packages into a detailed undergraduate textbook for the new syllabus. The original explanations, illustrated installation guide, step-by-step R commands, import lessons, descriptive-statistics examples, and time-series walkthrough form the main teaching text. Agricultural applications and additional worked examples extend those lessons. Data cleaning, modern graphics, growth models, inference, regression, and reproducible reporting complete the seven modules. The aim is to explain what each calculation does, why we use it, and how its result should be interpreted.
How to study this book
Read an explanation, predict what its code will do, run the code, and compare the output with your prediction. Change one input and explain the result. At the end of each practical, submit both your script and a short interpretation. Code that runs is necessary; a defensible explanation is equally necessary.
The text assumes basic arithmetic, elementary algebra, and introductory statistical concepts. It explains the additional ideas needed for each analysis. No previous programming experience is required. R 4.1 or later is needed for the native pipe |> used throughout.
PlantGrowth, Orange, and iris are datasets distributed with R; their help pages document their origins. The original historical time-series examples are also retained with local data copies and documented sources. The book supplies clearly labelled simulated crop, soil, weather, growth, and pest data for agricultural practice. Simulated results illustrate methods and must never be reported as findings from a field experiment. The data appendix records units, observational units, and limitations.
A recurring scientific workflow
- State a question and identify the observational or experimental unit.
- Inspect the design, variable definitions, units, and missing values.
- Clean data with a record of every decision.
- Summarize and visualize before fitting models.
- Select an analysis consistent with the question and design.
- Check assumptions and communicate uncertainty and limitations.
- Save the code, data provenance, and report so another person can reproduce the work.
This is an editable teaching edition. Instructors should review local terminology, course assessment rules, and the agricultural suitability of examples before adopting it. The earlier project’s licence files are preserved with the source archive; this edition does not assign a new licence.
Learn by running R
Each teaching chapter now ends with a Live R laboratory: editable code, a Run R button, console output, and plots where appropriate. Start with the first live calculation, or explore growth curves and regression. The browser downloads R on first use. Use a current browser and allow time for the initial download.
Every ordinary code block also has a copy button. Copy commands into RStudio without console prompts or printed results. The full package-based analyses and report rendering are intended for your installed R environment.
The reading layout follows the companion Textbook of Agricultural Statistics: serif explanations, blue headings, gold navigation highlights, and coloured learning boxes. Your original detailed lecture explanations remain the foundation of this edition.