This textbook covers R data analysis related to environmental science, starting with basic examples and proceeding up to advanced applications of the R programming language. The main objective of the textbook is to serve as a guide for undergraduate students, who have no previous experience with R, but part of the textbook is dedicated to advanced R applications, and will also be useful for Masters and PhD students, and professionals. The textbook deals with solving specific programming tasks in R, and tasks are organized in terms of gradually increasing R proficiency, with examples getting more challenging as the chapters progress. The main competencies students will acquire from this textbook are: manipulating and processing data tables performing statistical tests creating maps in R This textbook will be useful in undergraduate and graduate courses in Advanced Landscape Ecology, Analysis of Ecological and Environmental Data, Ecological Modeling, Analytical Methods for Ecologists, Statistical Inference for Applied Research, Elements of Statistical Methods, Computational Ecology, Landscape Metrics and Spatial Statistics.

This textbook covers R data analysis related to environmental science, starting with basic examples and proceeding up to advanced applications of the R programming language. The main objective of the textbook is to serve as a guide for undergraduate students, who have no previous experience with R, but part of the textbook is dedicated to advanced R applications, and will also be useful for Masters and PhD students, and professionals. The textbook deals with solving specific programming tasks in R, and tasks are organized in terms of gradually increasing R proficiency, with examples getting more challenging as the chapters progress. The main competencies students will acquire from this textbook are: manipulating and processing data tables performing statistical tests creating maps in R This textbook will be useful in undergraduate and graduate courses in Advanced Landscape Ecology, Analysis of Ecological and Environmental Data, Ecological Modeling, Analytical Methods for Ecologists, Statistical Inference for Applied Research, Elements of Statistical Methods, Computational Ecology, Landscape Metrics and Spatial Statistics.