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Inhaltsangabe
Introduction to Forestry Data Analysis with R equips students and practitioners with the skills needed to move confidently between field measurements and modern analytical workflows. As forestry, ecology, and natural resource management become increasingly data-driven, professionals are expected not only to collect information but also to organize, analyze, visualize, and defend quantitative results. This book responds to that shift by integrating foundational forest inventory concepts with practical computing in R.
Distinct from both generic programming texts and traditional mensuration references, this volume teaches R through real forestry datasets and operational examples. The first half develops core programming skills – data wrangling, visualization, and reproducible workflows – while the second half applies these tools to forest inventory, monitoring, and estimation. Classical methods developed by forest biometricians are presented alongside transparent, step-by-step computational implementations, enabling readers to connect statistical theory with modern, repeatable analysis.
Key Features:
· Introduces R and the tidyverse using forestry-specific datasets and management questions
· Develops reproducible workflows for data import, cleaning, transformation, visualization, and reporting
· Presents forest inventory concepts including simple random, systematic, stratified, cluster, and multistage sampling
· Implements classical estimators using transparent, script-based computing rather than black-box software
· Integrates spatial data handling and mapping for areal sampling frames and field-based applications
· Emphasizes practical problem-solving, code organization, and analytical habits that scale from single stands to large inventories
Introduction to Forestry Data Analysis with R is intended for undergraduate and graduate students in forestry, natural resources, and environmental science, as well as practitioners seeking to modernize and streamline their analytical workflows. Whether readers are learning R for the first time or adapting established inventory methods to contemporary datasets, it provides a clear, practical, and reproducible foundation for data-driven forest analysis.
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Über die Autorin bzw. den Autor
Andrew O. Finley is a Professor in the Department of Forestry and the Department of Statistics and Probability at Michigan State University, USA.
Jeffrey W. Doser is an Assistant Professor in the Department of Forestry and Environmental Resources at North Carolina State University, USA.
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Introduction to Forestry Data Analysis with R (Chapman & Hall/CRC The R Series)
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Hardcover. Zustand: new. Hardcover. Introduction to Forestry Data Analysis with R equips students and practitioners with the skills needed to move confidently between field measurements and modern analytical workflows. As forestry, ecology, and natural resource management become increasingly data-driven, professionals are expected not only to collect information but also to organize, analyze, visualize, and defend quantitative results. This book responds to that shift by integrating foundational forest inventory concepts with practical computing in R.Distinct from both generic programming texts and traditional mensuration references, this volume teaches R through real forestry datasets and operational examples. The first half develops core programming skillsdata wrangling, visualization, and reproducible workflowswhile the second half applies these tools to forest inventory, monitoring, and estimation. Classical methods developed by forest biometricians are presented alongside transparent, step-by-step computational implementations, enabling readers to connect statistical theory with modern, repeatable analysis.Key Features:Introduces R and the tidyverse using forestry-specific datasets and management questionsDevelops reproducible workflows for data import, cleaning, transformation, visualization, and reportingPresents forest inventory concepts including simple random, systematic, stratified, cluster, and multistage samplingImplements classical estimators using script-based computingIntegrates spatial data handling and mapping for areal sampling frames and field-based applicationsEmphasizes practical problem-solving, code organization, and analytical habits that scale from single stands to large inventoriesIntroduction to Forestry Data Analysis with R is intended for undergraduate and graduate students in forestry, natural resources, and environmental science, as well as practitioners seeking to modernize and streamline their analytical workflows. Whether readers are learning R for the first time or adapting established inventory methods to contemporary datasets, it provides a clear, practical, and reproducible foundation for data-driven forest analysis. As forestry, ecology, and natural resource management become increasingly data-driven, professionals are expected not only to collect information but also to organize, analyze, visualize, and defend quantitative results. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9781032120287
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Zustand: New. Andrew O. Finley is a Professor in the Department of Forestry and the Department of Statistics and Probability at Michigan State University, USA.Jeffrey W. Doser is an Assistant Professor in the Department of Forestry and Environment. Bestandsnummer des Verkäufers 2969284583
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Hardcover. Zustand: new. Hardcover. Introduction to Forestry Data Analysis with R equips students and practitioners with the skills needed to move confidently between field measurements and modern analytical workflows. As forestry, ecology, and natural resource management become increasingly data-driven, professionals are expected not only to collect information but also to organize, analyze, visualize, and defend quantitative results. This book responds to that shift by integrating foundational forest inventory concepts with practical computing in R.Distinct from both generic programming texts and traditional mensuration references, this volume teaches R through real forestry datasets and operational examples. The first half develops core programming skillsdata wrangling, visualization, and reproducible workflowswhile the second half applies these tools to forest inventory, monitoring, and estimation. Classical methods developed by forest biometricians are presented alongside transparent, step-by-step computational implementations, enabling readers to connect statistical theory with modern, repeatable analysis.Key Features:Introduces R and the tidyverse using forestry-specific datasets and management questionsDevelops reproducible workflows for data import, cleaning, transformation, visualization, and reportingPresents forest inventory concepts including simple random, systematic, stratified, cluster, and multistage samplingImplements classical estimators using script-based computingIntegrates spatial data handling and mapping for areal sampling frames and field-based applicationsEmphasizes practical problem-solving, code organization, and analytical habits that scale from single stands to large inventoriesIntroduction to Forestry Data Analysis with R is intended for undergraduate and graduate students in forestry, natural resources, and environmental science, as well as practitioners seeking to modernize and streamline their analytical workflows. Whether readers are learning R for the first time or adapting established inventory methods to contemporary datasets, it provides a clear, practical, and reproducible foundation for data-driven forest analysis. As forestry, ecology, and natural resource management become increasingly data-driven, professionals are expected not only to collect information but also to organize, analyze, visualize, and defend quantitative results. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Bestandsnummer des Verkäufers 9781032120287
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Hardcover. Zustand: new. Hardcover. Introduction to Forestry Data Analysis with R equips students and practitioners with the skills needed to move confidently between field measurements and modern analytical workflows. As forestry, ecology, and natural resource management become increasingly data-driven, professionals are expected not only to collect information but also to organize, analyze, visualize, and defend quantitative results. This book responds to that shift by integrating foundational forest inventory concepts with practical computing in R.Distinct from both generic programming texts and traditional mensuration references, this volume teaches R through real forestry datasets and operational examples. The first half develops core programming skillsdata wrangling, visualization, and reproducible workflowswhile the second half applies these tools to forest inventory, monitoring, and estimation. Classical methods developed by forest biometricians are presented alongside transparent, step-by-step computational implementations, enabling readers to connect statistical theory with modern, repeatable analysis.Key Features:Introduces R and the tidyverse using forestry-specific datasets and management questionsDevelops reproducible workflows for data import, cleaning, transformation, visualization, and reportingPresents forest inventory concepts including simple random, systematic, stratified, cluster, and multistage samplingImplements classical estimators using script-based computingIntegrates spatial data handling and mapping for areal sampling frames and field-based applicationsEmphasizes practical problem-solving, code organization, and analytical habits that scale from single stands to large inventoriesIntroduction to Forestry Data Analysis with R is intended for undergraduate and graduate students in forestry, natural resources, and environmental science, as well as practitioners seeking to modernize and streamline their analytical workflows. Whether readers are learning R for the first time or adapting established inventory methods to contemporary datasets, it provides a clear, practical, and reproducible foundation for data-driven forest analysis. As forestry, ecology, and natural resource management become increasingly data-driven, professionals are expected not only to collect information but also to organize, analyze, visualize, and defend quantitative results. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9781032120287
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