Nathan Whitmore

R for Conservation and Development Projects (eBook, PDF)

A Primer for Practitioners

• Format: PDF

Produktbeschreibung

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

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 394
• Erscheinungstermin: 21. Dezember 2020
• Englisch
• ISBN-13: 9780429552786
• Artikelnr.: 60763876

Autorenporträt

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