Introduction to R for Terrestrial Ecology
Basics of Numerical Analysis, Mapping, Statistical Tests and Advanced Application of R
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Beschreibung
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 LandscapeEcology, 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.
Introduces the problems and analysis of terrestrial ecology, and how R can help solve them Comprehensive tutorial begins with installation of the R software, demonstrates how to process data tables, proceeds to processing laboratory results and creating maps, and finally delves into advanced topics such as Supervised Learning Algorithms, Unsupervised Learning Algorithms, and Machine Learning For each chapter, input data that are needed for solving the specific tasks will be provided on Google Drive or similar web-based platform
Autor*in
Milena Lakicevic
Themen in »Introduction to R for Terrestrial Ecology«
maps R programming language ecological modelling environmental data landscape ecology statistical methods computational ecology terrestrial ecology Supervised Learning Algorithms rgbif environmental science
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“It can be used also by students and professionals in other scientific fields like biology, forestry and agriculture.” (Pavel Stoynov, zbMATH 1467.62002, 2021)
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