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Pawlik, Łukasz.

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R applications in earth sciences = from soil data to climate time series analysis and modeling /

Record Type:	Electronic resources : Monograph/item
Title/Author:	R applications in earth sciences/ by Łukasz Pawlik.
Reminder of title:	from soil data to climate time series analysis and modeling /
Author:	Pawlik, Łukasz.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xiv, 174 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Chapter 1: Introduction -- Chapter 2: R for data science - Functionality and basic concepts -- Chapter 3: Soil data structure, properties, and visualization -- Chapter 4: Geochemistry of fossil deposits and climate reconstructions based on proxy data -- Chapter 5: Climate time series -- Chapter 6: Geomorphic data and geomorphometry analyses -- Chapter 7: Dating the past with the radiocarbon method -- Chapter 8: Global tectonics and earthquake dynamics - From data to visualization -- Chapter 9: Land and forest cover change mapping -- Chapter 10: Extreme climate event modeling and prediction with machine learning methods -- Chapter 11: Knowledge in the cloud - LiDAR point cloud data -- Chapter 12: Dynamic visualization and animation -- Chapter 13: Final comments, conclusions, and data science perspective.
Contained By:	Springer Nature eBook
Subject:	Earth sciences - Data processing. -
Online resource:	https://doi.org/10.1007/978-3-031-89673-6
ISBN:	9783031896736

R applications in earth sciences = from soil data to climate time series analysis and modeling /
Pawlik, Łukasz.

R applications in earth sciencesfrom soil data to climate time series analysis and modeling /[electronic resource] :by Łukasz Pawlik. - Cham :Springer Nature Switzerland :2025. - xiv, 174 p. :ill. (some col.), digital ;24 cm. - Springer textbooks in earth sciences, geography and environment,2510-1315. - Springer textbooks in earth sciences, geography and environment..

Chapter 1: Introduction -- Chapter 2: R for data science - Functionality and basic concepts -- Chapter 3: Soil data structure, properties, and visualization -- Chapter 4: Geochemistry of fossil deposits and climate reconstructions based on proxy data -- Chapter 5: Climate time series -- Chapter 6: Geomorphic data and geomorphometry analyses -- Chapter 7: Dating the past with the radiocarbon method -- Chapter 8: Global tectonics and earthquake dynamics - From data to visualization -- Chapter 9: Land and forest cover change mapping -- Chapter 10: Extreme climate event modeling and prediction with machine learning methods -- Chapter 11: Knowledge in the cloud - LiDAR point cloud data -- Chapter 12: Dynamic visualization and animation -- Chapter 13: Final comments, conclusions, and data science perspective.

This textbook helps to understand the real Earth data with the practical application of many handy R tools and techniques. R language and thousands of R packages can be used to solve the most sophisticated scientific problems. The book provides insights to the various approaches to Earth-related data analysis, starting from data preparation and validation, exploratory data analysis, linear regression, and going through time series decomposition, modeling, and prediction. In addition, the book introduces machine learning techniques and their application to some real problems. Along with a profound explanation of the datasets and theoretical considerations of the methods, the book offers a way of solving practical problems lying at the frontline of modern data analysis in physical geography, soils, and climate science.

ISBN: 9783031896736

Standard No.: 10.1007/978-3-031-89673-6doiSubjects--Topical Terms:

544097
Earth sciences
--Data processing.

LC Class. No.: QE48.8

Dewey Class. No.: 550.285

R applications in earth sciences = from soil data to climate time series analysis and modeling /
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520 $a This textbook helps to understand the real Earth data with the practical application of many handy R tools and techniques. R language and thousands of R packages can be used to solve the most sophisticated scientific problems. The book provides insights to the various approaches to Earth-related data analysis, starting from data preparation and validation, exploratory data analysis, linear regression, and going through time series decomposition, modeling, and prediction. In addition, the book introduces machine learning techniques and their application to some real problems. Along with a profound explanation of the datasets and theoretical considerations of the methods, the book offers a way of solving practical problems lying at the frontline of modern data analysis in physical geography, soils, and climate science.
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