A Beginner’s Guide to Data Exploration and Visualisation with R

TOC
Chapter1
1.1 Speaking the same language
1.2. General points
1.3 Outline of this book
Chapter2
2.1 What is an outlier?
2.2 Boxplot to identify outliers in one dimension
2.3 Cleveland dotplot to identify outliers in onedimension
2.4 Boxplots or Cleveland dotplots?
2.5 Can we apply a test for outliers?
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
Chapter3
3.1 What is normality?
3.2 Histograms and conditional histograms
3.3 Kernel density plots
3.4 Quantile–quantile plots
3.5 Using tests to check for normality
3.6 Homogeneity of variance
3.7 Using tests to check for homogeneity
Chapter4
4.1 Simple scatterplots
4.2 Multipanel scatterplots
4.3 Pairplots
4.4 Can we include interactions?
4.5 Design and interaction plots
Chapter5
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 variance inflation factors
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 in the model?
5.13 Confounding variables
Chapter6
6.1 Introduction
6.2 Data exploration
6.3 Statistical analysis using linear regression
6.4 Statistical analysis using a mixed effects model
6.5 Conclusions
6.6 What to present in a paper
Chapter7
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 Conclusions
Chapter8
8.1 Importing the data
8.2 Coding the data
8.3 Multi-panel graph using xyplot from lattice
8.4 Multi-panel graph using ggplot2
8.5 Conclusions
References
Index
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