A beginner's guide to data exploration and visualisation with R

A beginner's guide to data exploration and visualisation with R

This book uses ecological datasets to discuss data exploration and visualisation tools. The authors also explain how to visualise the results of statistical models, an important aspect for publishing scientific papers. The book includes the R code needed to construct, visualise, and explore the main features of the data step by step.

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Bibliographic Details
Main Authors: Ieno, Elena N. autor/a, Zuur, Alain F. autor/a
Format: Texto biblioteca
Language:eng
Published: Newburgt, United Kingdom Highland Statistics Ltd 2015
Subjects:R (Lenguaje de programación para computadora), Procesamiento de datos, Estadística matemática, Ecología,
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id KOHA-OAI-ECOSUR:2616
record_format koha
institution ECOSUR
collection Koha
country México
countrycode MX
component Bibliográfico
access En linea
Fisico
databasecode cat-ecosur
tag biblioteca
region America del Norte
libraryname Sistema de Información Bibliotecario de ECOSUR (SIBE)
language eng
topic R (Lenguaje de programación para computadora)
Procesamiento de datos
Estadística matemática
Ecología
R (Lenguaje de programación para computadora)
Procesamiento de datos
Estadística matemática
Ecología
spellingShingle R (Lenguaje de programación para computadora)
Procesamiento de datos
Estadística matemática
Ecología
R (Lenguaje de programación para computadora)
Procesamiento de datos
Estadística matemática
Ecología
Ieno, Elena N. autor/a
Zuur, Alain F. autor/a
A beginner's guide to data exploration and visualisation with R
description This book uses ecological datasets to discuss data exploration and visualisation tools. The authors also explain how to visualise the results of statistical models, an important aspect for publishing scientific papers. The book includes the R code needed to construct, visualise, and explore the main features of the data step by step.
format Texto
topic_facet R (Lenguaje de programación para computadora)
Procesamiento de datos
Estadística matemática
Ecología
author Ieno, Elena N. autor/a
Zuur, Alain F. autor/a
author_facet Ieno, Elena N. autor/a
Zuur, Alain F. autor/a
author_sort Ieno, Elena N. autor/a
title A beginner's guide to data exploration and visualisation with R
title_short A beginner's guide to data exploration and visualisation with R
title_full A beginner's guide to data exploration and visualisation with R
title_fullStr A beginner's guide to data exploration and visualisation with R
title_full_unstemmed A beginner's guide to data exploration and visualisation with R
title_sort beginner's guide to data exploration and visualisation with r
publisher Newburgt, United Kingdom Highland Statistics Ltd
publishDate 2015
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AT zuuralainfautora abeginnersguidetodataexplorationandvisualisationwithr
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spelling KOHA-OAI-ECOSUR:26162023-03-18T12:26:38ZA beginner's guide to data exploration and visualisation with R Ieno, Elena N. autor/a Zuur, Alain F. autor/a textNewburgt, United Kingdom Highland Statistics Ltd2015engThis book uses ecological datasets to discuss data exploration and visualisation tools. The authors also explain how to visualise the results of statistical models, an important aspect for publishing scientific papers. The book includes the R code needed to construct, visualise, and explore the main features of the data step by step.Incluye bibliografía: páginas 155-158 e índice: páginas 159-160Preface.. Acknowledgements.. Datasets Used in This Book.. 1 Introduction.. 1.1 Speaking the Same Language.. 1.2. General Points.. 1.3 Outline of This Book.. 2 Outliers.. 2.1 What Is an Outlier?.. 2.2 Boxplot To Identify Outliers In One Dimension.. 2.2.1 Simple boxplot.. 2.2.2 Conditional boxplot.. 2.2.3 Multi-panel boxplots from the lattice package.. 2.3 Cleveland Dotplot To Identify Outliers.. 2.3.1 Simple Cleveland dotplots.. 2.3.2 Conditional Cleveland dotplots.. 2.3.3 Multi-panel Cleveland dotplots from the lattice package.. 2.4 Boxplots or Cleveland Dotplots?.. 2.5 Can We Apply a Test For Outliers?.. 2.5.1 Z-score.. 2.5.2 Grubbs' test.. 2.6 Outliers In the Two-Dimensional Space.. 2.7 Influential Observations In Regression Models.. 2.8 What to do if You Detect Potential Outliers.. 2.9 Outliers and Multivariate Data.. 2.10 The Pros and Cons of Transformations.. 3 Normality and Homogeneity.. 3.1 What Is Normality?.. 3.2 Histograms And Conditional Histograms.. 3.2.1 Multipanel histograms from the lattice package.. 3.2.2 When is normality of the raw data considered?.. 3.3 Kernel Density Plots.. 3.4 Quantile-Quantile Plots.. 3.4.1 Quantile-quantile plots from the lattice package.. 3.5 Using Tests to Check For Normality.. 3.6 Homogeneity of Variance.. 3.6.1 Conditional boxplots.. 3.6.2 Scatterplots for continuous explanatory variables.. 3.7 Using Tests to Check For Homogeneity.. 3.7.1 The Bartlett test.. 3.7.2 The F-ratio test.. 3.7.3 Levene's test.. 3.7.4 So which test would you choose?.. 3.7.5 R code.. 3.7.6 Using graphs?.. 4 Relationships.. 4.1 Simple Scatterplots.. 4.1.1 Example: Clam data.. 4.1.2 Example: Rabbit data.. 4.1.3 Example: Blow fly data.. 4.2 Multipanel Scatterplots.. 4.2.1 Example: Polychaeta data.. 4.2.2 Example: Bioluminescence data.. 4.3 Pairplots.. 4.3.1 Bioluminescence data.. 4.3.2 Cephalopod data.. 4.3.3 Zoobenthos data.. 4.4 Can We Include Interactions?.. 4.4.1 Irish pH data4.4.2 Godwit data.. 4.4.3 Irish pH data revisited.. 4.4.4 Parasite data.. 4.5 Design and Interaction Plots.. 5 Collinearity and Confounding.. 5.1 What is Collinearity?.. 5.2 The Sample Correlation Coefficient.. 5.3 Correlation and Outliers.. 5.4 Correlation Matrices.. 5.5 Correlation and Pairplots.. 5.6 Collinearity Due To Interactions.. 5.7 Visualising Collinearity With Conditional Boxplots.. 5.8 Quantifying Collinearity Using Vifs.. 5.8.1 Variance inflation factors.. 5.8.2 Geometric presentation of collinearity.. 5.8.3 Tolerance.. 5.8.4 What constitutes a high VIF value?.. 5.8.5 VIFs in action.. 5.9 Generalised Vif Values.. 5.10 Visualising Collinearity Using Pca Biplot.. 5.11 Causes of Collinearity And Solutions.. 5.12 Be Stubborn and Keep Collinear Covariates?.. 5.13 Confounding Variables.. 5.13.1 Visualising confounding variables.. 5.13.2 Confounding factors in time series analysis.. 6 Case Study: Methane Fluxes.. 6.1 Introduction.. 6.2 Data Exploration.. 6.2.1 Where in the world are the sites?.. 6.2.2 Working with ggplot2.. 6.2.3 Outliers.. 6.2.4 Collinearity.. 6.2.5 Relationships.. 6.2.6 Interactions 6.2.7 Where in the world are the sites (continued?.. 6.3 Statistical Analysis Using Linear Regression.. 6.3.1 Model formulation.. 6.3.2 Fitting a linear regression model.. 6.3.3 Model validation of the linear regression model.. 6.3.4 Interpretation of the linear regression model.. 6.4 Statistical Analysis Using a Mixed Effects Model.. 6.4.1 Model formulation.. 6.4.2 Fitting a mixed effects model.. 6.4.3 Model validation of the mixed effects model.. 6.4.4 Interpretation of the linear mixed effects model.. 6.5 Conclusions.. 6.6 What To Present In a Paper.. 7 Case Study: Oystercatcher Shell Length.. 7.1 Importing the Data.. 7.2 Data Exploration.. 7.3 Applying A Linear Regression Model.. 7.4 Understanding The Results.. 7.5 Trouble.. 7.6 Conclusions8 Case Study: Hawaiian Bird Time Series.. 8.1 Importing the Data.. 8.2 Coding the Data.. 8.3 Multi-Panel Graph Using Xyplot From Lattice.. 8.3.1 Attempt 1 using xyplot.. 8.3.2 Attempt 2 using xyplot.. 8.3.3 Attempt 3 using xyplot.. 8.4 Multi-Panel Graph Using Ggplot2.. 8.5 Conclusions.. References.. Index.. Books by Highland StatisticsThis book uses ecological datasets to discuss data exploration and visualisation tools. The authors also explain how to visualise the results of statistical models, an important aspect for publishing scientific papers. The book includes the R code needed to construct, visualise, and explore the main features of the data step by step.R (Lenguaje de programación para computadora)Procesamiento de datosEstadística matemáticaEcologíaURN:ISBN:0957174179URN:ISBN:9780957174177

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