R for Conservation and Development Projects A Primer for Practitioners.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Whitmore, Nathan
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Milton : CRC Press LLC, 2020.
Colección:Chapman and Hall/CRC the R Ser.
Temas:
R (Computer program language)
Mathematical statistics > Data processing.
Conservation projects (Natural resources) > Data processing.
R (Langage de programmation)
Statistique mathématique > Informatique.
Projets de conservation (Ressources naturelles) > Informatique.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Half Title
  • Series Page
  • Title Page
  • Copyright Page
  • Dedication
  • Contents
  • Preface
  • 1. Introduction
  • 1.1. What is R?
  • 1.2. Why R?
  • 1.3. Why this book?
  • 1.4. What are development and conservation?
  • 1.5. Science and decision making
  • 1.6. Why data science is important
  • 1.6.1. Monitoring and evaluation
  • 1.6.2. Projects versus programmes
  • 1.6.3. Project delivery versus research projects
  • 1.7. The goal of this book
  • 1.8. How this book is organised
  • 1.9. How code is organised in this book
  • I: Basics
  • 2. Inference and Evidence
  • 2.1. Inference
  • 2.2. Study design
  • 2.3. Evidence
  • 2.4. What makes good data?
  • 2.5. Recommended resources
  • 2.6. Summary
  • 3. Data integration in project management
  • 3.1. Adaptive management cycles
  • 3.2. The Deming cycle
  • 3.2.1. Plan
  • 3.2.1.1. Development of a project strategy and proposal
  • 3.2.1.2. Proposal submission process
  • 3.2.1.3. What is a logframe?
  • 3.2.1.4. Logframe terminology
  • 3.2.1.5. Pre-implementation planning
  • 3.2.2. Train
  • 3.2.3. Do
  • 3.2.4. Check
  • 3.2.5. Act
  • 3.3. Challenges
  • 3.4. Recommended resources
  • 3.5. Summary
  • 4. Getting started in R
  • 4.1. Installing R
  • 4.2. Installing RStudio
  • 4.3. The R interface
  • 4.3.1. The console
  • 4.3.2. Version information
  • 4.3.3. Writing code in the console
  • 4.3.4. Script editors
  • 4.3.5. Using the default script editor
  • 4.3.6. Using RStudio
  • 4.4. R as a calculator
  • 4.5. How R works
  • 4.5.1. Objects
  • 4.5.2. Functions
  • 4.5.2.1. Getting help on functions
  • 4.5.3. Packages
  • 4.5.3.1. Getting help on packages
  • 4.6. Writing meaningful code
  • 4.7. Reproducibility
  • 4.8. Recommended resources
  • 4.9. Summary
  • 5. Introduction to data frames
  • 5.1. Making data frames
  • 5.2. Importing a data frame
  • 5.3. Saving a data frame
  • 5.4. Investigating a data frame
  • 5.5. Other functions to examine an R object
  • 5.6. Subsetting using the `[' and `]' operators
  • 5.7. Descriptive statistics
  • 5.8. Viewing data frames
  • 5.9. Making a reproducible example
  • 5.9.1. Reproducible example steps
  • 5.10. Recommended resources
  • 5.11. Summary
  • 6. The Waihi project
  • 6.1. The scenario
  • 6.1.1. Why evidence is important
  • 6.2. The data
  • 6.2.1. Description of condev data sets
  • 6.3. Recommended resources
  • 6.4. Summary
  • II: First steps
  • 7. ggplot2: graphing with the tidyverse
  • 7.1. Why graph?
  • 7.2. The tidyverse package
  • 7.3. The data
  • 7.4. Graphing in R
  • 7.4.1. Making a ggplot
  • 7.4.2. Scatter plots
  • 7.4.3. Bar plots
  • 7.4.4. Histograms
  • 7.4.5. Box plots
  • 7.4.6. Polygons
  • 7.4.7. Other common geoms
  • 7.5. How to save a ggplot
  • 7.6. Recommended resources
  • 7.7. Summary
  • 8. Customising a ggplot
  • 8.1. Why customise a ggplot?
  • 8.2. The packages
  • 8.3. The data
  • 8.4. Families of layers
  • 8.5. Aesthetics properties
  • 8.5.1. Settings aesthetics
  • 8.5.2. A quick note about colour

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