Introduction to R for Terrestrial Ecology: Basics of Numerical Analysis, Mapping, Statistical Tests and Advanced Application of R

Acknowledgments\nAbout the Book\nContents\nAbout the Authors\nList of Figures\nList of Tables\n1: Types of Data in R\n	1.1	 Vectors\n		1.1.1	 Numerical Vectors\n			1.1.1.1	 Creating a Numerical Vector\n			1.1.1.2	 Manipulating a Numerical Vector\n		1.1.2	 Character Vectors\n		1.1.3	 Logical Vectors\n		1.1.4	 Selecting, Removing, and Adding Elements with Vectors\n			1.1.4.1	 Selecting Elements from a Vector\n			1.1.4.2	 Removing Elements from a Vector\n			1.1.4.3	 Adding Elements to a Vector\n	1.2	 Matrices\n		1.2.1	 Manipulating a Matrix\n	1.3	 Data Frames and Lists\n		1.3.1	 Creating a Data Frame\n		1.3.2	 Selecting Data Within Data Frames\n		1.3.3	 Removing Elements from a Data Frame\n		1.3.4	 Adding Elements to a Data Frame\n		1.3.5	 Additional Operations on Data Frames\n	1.4	 Missing Data\n		1.4.1	 Removing Missing Values\n		1.4.2	 Replacing Missing Values\n	1.5	 Outliers\n		1.5.1	 Transforming Outliers\n		1.5.2	 Removing Outliers\n2: Numerical Analysis\n	2.1	 Basic Numerical Analyses\n		2.1.1	 Determining Biological Spectrum\n		2.1.2	 Determining Share of Species Within a Community\n	2.2	 Advanced Numerical Analyses\n		2.2.1	 Biodiversity Indices\n			2.2.1.1	 Calculating Alpha Biodiversity Indices with Formulas\n				2.2.1.1.1 Shannon Index\n				2.2.1.1.2 Simpson Index\n			2.2.1.2	 Calculating Alpha Biodiversity Indices with the “Vegan” Packages\n				2.2.1.2.1 Richness\n				2.2.1.2.2 Shannon Index\n				2.2.1.2.3 Simpson Index\n			2.2.1.3	 Calculating Beta Biodiversity Indices with the “Vegan” Package\n				2.2.1.3.1 Sorensen Index\n				2.2.1.3.2 Jaccard Distance\n		2.2.2	 Additional Examples\n			2.2.2.1	 Aggregating Values\n			2.2.2.2	 Statistical Parameters\n			2.2.2.3	 Clustering\n			2.2.2.4	 Gathering Results\n3: Creating Maps\n	3.1	 Number of National Parks in the US\n		3.1.1	 Number of National Parks: Selecting States\n	3.2	 Locations of Endemic Species in Serbia\n	3.3	 Package “rgbif”\n		3.3.1	 Occurrence of Single Species\n		3.3.2	 Occurrence of More than One Species\n4: Basic Statistical Tests\n	4.1	 Independent t-test\n	4.2	 Dependent t-test\n	4.3	 One-Way ANOVA\n	4.4	 Two-Way ANOVA\n5: Predictive Modeling with Machine Learning Applications\n	5.1	 Machine Learning Basics\n	5.2	 Model Assessment\n	5.3	 Machine Learning Algorithms\n		5.3.1	 Decision Trees\n		5.3.2	 Random Forest\n		5.3.3	 Boosted Regression Trees\n		5.3.4	 Stacked Ensemble\n	5.4	 Case Study: Modeling the Distribution of Western Larch (Larix occidentalis) Under Recent Past and Future Climate Across Western North America\n		5.4.1	 Generating Hypotheses\n		5.4.2	 Data Gathering and Preparation\n		5.4.3	 Model Development\n		5.4.4	 Model Assessment\n		5.4.5	 Model Prediction to Historical and Future Climates\n		5.4.6	 Further Reading\nAppendix 1\nAppendix 2\nAppendix 3\nList of the R Packages Used in the Book (Alphabetical Order)\nReferences