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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.
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