Jerry Douglas Davis is a Professor of Geography & Environment (https://geog.sfsu.edu/) and the Director of the Institute for Geographic Information Science (https://gis.sfsu.edu/) at San Francisco State University, and borrows heavily from his and his students' field-based environmental research for examples in the book.

• 1. Background, Goals & Data Part 1: Exploratory Data Analysis 2. Introduction to R 3. Data Abstraction 4. Visualization 5. Data Transformation Part 2: Spatial 6. Spatial Data and Maps 7. Spatial Analysis 8. Raster Spatial Analysis 9. Spatial Interpolation Part 3: Statistics & Modeling 10. Statistical Summaries and Tests 11. Modeling 12. Imagery and Classification Models Part 4: Time Series 13. Time Series Visualization & Analysis Part 5: Communication and References 14. Communication with Shiny

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Jerry Douglas Davis is a Professor of Geography & Environment (https://geog.sfsu.edu/) and the Director of the Institute for Geographic Information Science (https://gis.sfsu.edu/) at San Francisco State University, and borrows heavily from his and his students' field-based environmental research for examples in the book.

• 1. Background, Goals & Data Part 1: Exploratory Data Analysis 2. Introduction to R 3. Data Abstraction 4. Visualization 5. Data Transformation Part 2: Spatial 6. Spatial Data and Maps 7. Spatial Analysis 8. Raster Spatial Analysis 9. Spatial Interpolation Part 3: Statistics & Modeling 10. Statistical Summaries and Tests 11. Modeling 12. Imagery and Classification Models Part 4: Time Series 13. Time Series Visualization & Analysis Part 5: Communication and References 14. Communication with Shiny

Select Guide Rating

Jerry Douglas Davis is a Professor of Geography & Environment (https://geog.sfsu.edu/) and the Director of the Institute for Geographic Information Science (https://gis.sfsu.edu/) at San Francisco State University, and borrows heavily from his and his students' field-based environmental research for examples in the book.

• 1. Background, Goals & Data Part 1: Exploratory Data Analysis 2. Introduction to R 3. Data Abstraction 4. Visualization 5. Data Transformation Part 2: Spatial 6. Spatial Data and Maps 7. Spatial Analysis 8. Raster Spatial Analysis 9. Spatial Interpolation Part 3: Statistics & Modeling 10. Statistical Summaries and Tests 11. Modeling 12. Imagery and Classification Models Part 4: Time Series 13. Time Series Visualization & Analysis Part 5: Communication and References 14. Communication with Shiny

Select Guide Rating