# Contents

Note that the spatial part of this web site starts in section 5. This is because we need to have some background in using R before getting into spatial statistics. Jumping ahead is okay, just be ready to go back a bit if you don't recognize something right away.

## 1. Getting Started

The first lessons below will introduce you to using R. These lessons are not focused on spatial data but on the basic operations of R you'll need for working with Spatial Data.

- About R
- Installing
- Introduction To R and R Studio
- Getting Started with R
- Writing Scripts
- Getting Help

## 2. Exploring Tabular Data

These lessons will show you how to create data in R and use R to explore data.

- Generating Data
- Filtering Datatables
- Linear Regression
- Histograms
- Testing Normality (QQPlots)
- Testing for Covariance
- 2D Plots
- Files and Folders
- 3D Plots
- Combined Model Plot

## 3. Control Flow

## 4. Packages and Libraries

## 5. Working With Spatial Data

The packages for spatial data are rather complex and in a state of transition from older ones to newer ones. I recommend checking out this web page before continuing. The pages below start with approaches that use CSV files with points and then get into the most reliable packages I have found.

- Introduction
- Point Data (CSVs) as Data Frames
- Reading Raster File Formats (and other raster operations)
- Note that rgdal (rgdal is going away in 2023)

- Working with Vector Data in sf
- Converting columns
- Cluster Analysis

## 6. Interpolation

- Interpolation
- Variograms & Kriging
- Spatial Statistics (incomplete)

## 7. Correlation/Regression Models

- Generalized Linear Models (GLMs)
- Generalized Additive Models (GAMs)
- Categorical and Regression Trees (CART)
- Habitat Suitability Models
- Presence-Only Example with GAMs

## 8. Monte Carlo Methods

- Introduction to Monte Carlo methods
- Sub-Sampling Data for Cross-Validation
- Noise Injection
- Estimating Uncertainty in Raster Covariates
- Creating Uncertianty Maps

## 9. Additional Information

## Acknowledgments

The material in this web site was drawn from a large number of web sites, blogs, articles, and books. Special thanks go to Jose Montero for contributing content.