Nathan Whitmore is a conservation scientist and practitioner who worked previously as a fisheriesobserver, and a ranger for the New Zealand Department of Conservation. Between 2012 and 2018he worked in Papua New Guinea where he provided scientific and project support for the WildlifeConservation Society’s Papua New Guinea programme. His professional interests include evidence-based decision making, sustainable use of wildlife, and traditional natural resource management.

"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

• 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

Nathan Whitmore is a conservation scientist and practitioner who worked previously as a fisheriesobserver, and a ranger for the New Zealand Department of Conservation. Between 2012 and 2018he worked in Papua New Guinea where he provided scientific and project support for the WildlifeConservation Society’s Papua New Guinea programme. His professional interests include evidence-based decision making, sustainable use of wildlife, and traditional natural resource management.

"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

• 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

Nathan Whitmore is a conservation scientist and practitioner who worked previously as a fisheriesobserver, and a ranger for the New Zealand Department of Conservation. Between 2012 and 2018he worked in Papua New Guinea where he provided scientific and project support for the WildlifeConservation Society’s Papua New Guinea programme. His professional interests include evidence-based decision making, sustainable use of wildlife, and traditional natural resource management.

"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

• 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