Nathan Whitmore is a conservation scientist and practitioner who worked previously as a fisheries
observer, and a ranger for the New Zealand Department of Conservation. Between 2012 and 2018
he worked in Papua New Guinea where he provided scientific and project support for the Wildlife
Conservation Society´s Papua New Guinea programme. His professional interests include evidence-
based decision making, sustainable use of wildlife, and traditional natural resource management.
- Simple English format
- Foundation sections on inference and evidence, and data integration in project management
- Exploration of R usage through a narrative examining a generic integrated conservation and development project
- A final section on R for reproducible workflow
- Accompanied by an R package
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This book is aimed at conservation and development practitioners who need to learn and use R in a part-time professional context. It gives people with a non-technical background a set of skills to graph, map, and model in R. It also provides background on data integration in project management and covers fundamental statistical concepts. The book aims to demystify R and give practitioners the confidence to use it.
Key Features:
- Viewing data science as part of a greater knowledge and decision making system
- Foundation sections on inference, evidence, and data integration
- Plain English explanations of R functions
- Relatable examples which are typical of activities undertaken by conservation and development organisations in the developing world
- Worked examples showing how data analysis can be incorporated into project reports
1. Introduction 2. Inference and Evidence 3. Data integration in project management 4. Getting started in R 5. Introduction to data frames 6. The Waihi project 7. ggplot2: graphing with the tidyverse 8. Customising a ggplot 9. Data wrangling 10. Data cleaning 11. Working with dates and time 12. Working with spatial data 13. Common R code mistakes and quirks 14. Basic statistical concepts 15. Understanding linear models 16. Extensions to linear models 17. Introduction to clustering and classification 18. Reporting and worked examples 19. Epilogue
This book is bridging the gap for organisations and individuals who need to learn and use R in a part-time professional context, providing a set of skills to understand the usefulness of graphing, mapping, and modelling in R, using relatable examples throughout.
"The author leads the reader smoothly through the chapters of Part I and II, giving exactly as much information as is needed. This is the biggest advantage of the book. The new information and the R codes are given in a short but comprehensive and thoughtful way. With few words, the reader receives a message which is clear and easy to understand."
- Kinga Salapa, International Society for Clinical Biostatistics, 72, 2021
Über den Autor
Nathan Whitmore is a conservation scientist and practitioner who worked previously as a fisheries
observer, and a ranger for the New Zealand Department of Conservation. Between 2012 and 2018
he worked in Papua New Guinea where he provided scientific and project support for the Wildlife
Conservation Society's Papua New Guinea programme. His professional interests include evidence-
based decision making, sustainable use of wildlife, and traditional natural resource management.
Inhaltsverzeichnis
1. Introduction 2. Inference and Evidence 3. Data integration in project management 4. Getting started in R 5. Introduction to data frames 6. The Waihi project 7. ggplot2: graphing with the tidyverse 8. Customising a ggplot 9. Data wrangling 10. Data cleaning 11. Working with dates and time 12. Working with spatial data 13. Common R code mistakes and quirks 14. Basic statistical concepts 15. Understanding linear models 16. Extensions to linear models 17. Introduction to clustering and classification 18. Reporting and worked examples 19. Epilogue
Klappentext
This book is bridging the gap for organisations and individuals who need to learn and use R in a part-time professional context, providing a set of skills to understand the usefulness of graphing, mapping, and modelling in R, using relatable examples throughout.
