Dylan Z. Childs's Books

Data-driven Modelling of Structured Populations

A Practical Guide to the Integral Projection Model

By: Stephen P. Ellner,Dylan Z. Childs,Mark Rees

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Data-driven Modelling of Structured Populations

A Practical Guide to the Integral Projection Model

By: Stephen P. Ellner,Dylan Z. Childs,Mark Rees

This book is a “How To” guide for modeling population dynamics using Integral Projection Models (IPM) starting from observational data. It is written by a leading research team in this area and includes code in the R language (in the text and online) to carry out all computations. The intended audience are ecologists, evolutionary biologists, and mathematical biologists interested in developing data-driven models for animal and plant populations. IPMs may seem hard as they involve integrals. The aim of this book is to demystify IPMs, so they become the model of choice for populations structured by size or other continuously varying traits. The book uses real examples of increasing complexity to show how the life-cycle of the study organism naturally leads to the appropriate statistical analysis, which leads directly to the IPM itself. A wide range of model types and analyses are presented, including model construction, computational methods, and the underlying theory, with the more technical material in Boxes and Appendices. Self-contained R code which replicates all of the figures and calculations within the text is available to readers on GitHub. Stephen P. Ellner is Horace White Professor of Ecology and Evolutionary Biology at Cornell University, USA; Dylan Z. Childs is Lecturer and NERC Postdoctoral Fellow in the Department of Animal and Plant Sciences at The University of Sheffield, UK; Mark Rees is Professor in the Department of Animal and Plant Sciences at The University of Sheffield, UK.

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Page Count

339

Publisher

Springer

Published Date

ISBN 10

3319288938

ISBN 13

9783319288932

Insights from Data with R

An Introduction for the Life and Environmental Sciences

By: Owen L. Petchey,Andrew P. Beckerman,Natalie Cooper,Dylan Z. Childs

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Insights from Data with R

An Introduction for the Life and Environmental Sciences

By: Owen L. Petchey,Andrew P. Beckerman,Natalie Cooper,Dylan Z. Childs

Experiments, surveys, measurements, and observations all generate data. These data can provide useful insights for solving problems, guiding decisions, and formulating strategy. Progressing from relatively unprocessed data to insight, and doing so efficiently, reliably, and confidently, does not come easily, and yet gaining insights from data is a fundamental skill for science as well as many other fields and often overlooked in most textbooks of statistics and data analysis. This accessible and engaging book provides readers with the knowledge, experience, and confidence to work with data and unlock essential information (insights) from data summaries and visualisations. Based on a proven and successful undergraduate course structure, it charts the journey from initial question, through data preparation, import, cleaning, tidying, checking, double-checking, manipulation, and final visualization. These basic skills are sufficient to gain useful insights from data without the need for any statistics; there is enough to learn about even before delving into that world! The book focuses on gaining insights from data via visualisations and summaries. The journey from raw data to insights is clearly illustrated by means of a comprehensive Workflow Demonstration in the book featuring data collected in a real-life study and applicable to many types of question, study, and data. Along the way, readers discover how to efficiently and intuitively use R, RStudio, and tidyverse software, learning from the detailed descriptions of each step in the instructional journey to progress from the raw data to creating elegant and informative visualisations that reveal answers to the initial questions posed. There are an additional three demonstrations online! Insights from Data with R is suitable for undergraduate students and their instructors in the life and environmental sciences seeking to harness the power of R, RStudio, and tidyverse software to master the valuable and prerequisite skills of working with and gaining insights from data.

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Page Count

272

Publisher

Oxford University Press

Published Date

ISBN 10

0192589733

ISBN 13

9780192589736

Getting Started with R

An Introduction for Biologists

By: Andrew P. Beckerman,Dylan Z. Childs,Owen L. Petchey

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Getting Started with R

An Introduction for Biologists

By: Andrew P. Beckerman,Dylan Z. Childs,Owen L. Petchey

R is rapidly becoming the standard software for statistical analyses, graphical presentation of data, and programming in the natural, physical, social, and engineering sciences. Getting Started with R is now the go-to introductory guide for biologists wanting to learn how to use R in their research. It teaches readers how to import, explore, graph, and analyse data, while keeping them focused on their ultimate goals: clearly communicating their data in oral presentations, posters, papers, and reports. It provides a consistent workflow for using R that is simple, efficient, reliable, and reproducible. This second edition has been updated and expanded while retaining the concise and engaging nature of its predecessor, offering an accessible and fun introduction to the packages dplyr and ggplot2 for data manipulation and graphing. It expands the set of basic statistics considered in the first edition to include new examples of a simple regression, a one-way and a two-way ANOVA. Finally, it introduces a new chapter on the generalised linear model. Getting Started with R is suitable for undergraduates, graduate students, professional researchers, and practitioners in the biological sciences.

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Page Count

251

Publisher

Oxford University Press

Published Date

ISBN 10

0198787839

ISBN 13

9780198787839

Book's by Dylan Z. Childs

Data-driven Modelling of Structured Populations

Data-driven Modelling of Structured Populations

Insights from Data with R

Insights from Data with R

Getting Started with R

Getting Started with R

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