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
Rezensionen / Stimmen
"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
Reihe
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Illustrationen
123 s/w Abbildungen, 123 s/w Zeichnungen, 7 s/w Tabellen
7 Tables, black and white; 123 Line drawings, black and white; 123 Illustrations, black and white
Maße
Höhe: 240 mm
Breite: 161 mm
Dicke: 26 mm
Gewicht
ISBN-13
978-0-367-20549-2 (9780367205492)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
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.
Autor*in
Independent Environmental Consultant - Dunedin, New Zealand
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
