University of Twente, 2013. — 256 p.This tutorial introduces the R environment for statistical computing and visualisation and its dialect of the S language. It is organized as a systematic analysis of a simple dataset: the Mercer & Hall wheat yield uniformity trial (Appendix A). After completing the tutorial you should:
know the basics of the R environment;
be able to use R at a beginning to intermediate level;
follow a systematic approach to analyze a simple dataset.The tutorial is organized as a set of tasks followed by questions to check your understanding; answers are at the end of each section. If you are ambitious, there are also some challenges: tasks and questions with no solution provided, that require the integration of skills learned in the section.Contents:Introduction
R basics
Leaving R
Answers
Loading and examining a data set
Reading a CSV _le into an R object
Examining a dataset
Saving a dataset in R format
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Exploratory graphics
Univariate exploratory graphics
Bivariate exploratory graphics
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Descriptive statistics
Other descriptive statistics*
Attaching a data frame to the search path
A closer look at the distribution
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Editing a data frame
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Univariate modelling
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Bivariate modelling: two continuous variables
Correlation
Univariate linear regression
Structural Analysis*
No-intercept model*
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Bivariate modelling: continuous response, classi_ed predictor
Exploratory data analysis
Two-sample t-test
One-way ANOVA
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Bootstrapping*
Example: 1% quantile of grain yield
Example: structural relation between grain and straw
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Robust methods*
A contaminated dataset
Robust univariate modelling
Robust bivariate modelling
Robust regression
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Multivariate modelling
Additive model: parallel regression
Comparing models
Interaction model
Regression diagnostics
Analysis of covariance: a nested model*
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Principal Components Analysis
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Model validation
Splitting the dataset
Developing the model
Predicting at the validation observations
Measures of model quality*
An inappropriate model form*
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Cross-validation*
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Spatial analysis
Geographic visualisation
Setting up a co ordinate system
Loading add-in packages
Creating a spatially-explicit object
More geographic visualisation
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Spatial structure
Spatial structure: trend
Spatial structure: local
Absence of spatial structure*
Spatial structure of _eld halves*
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Generalized least squares regression*
A detour into Maximum Likelihood*
Residual Maximum Likelihood
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Geographically-weighted regression*
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Periodicity*
Visualizing periodicity
Spectral analysis
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The e_ect of plot size*
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References
Index of R concepts
A Example Data Set
B Colours
