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.
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.
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.
Inhaltsangabe
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
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
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