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Tutorials on R and statistics

23 April 2011

This series of tutorials will gradually work through an extensive range of frequentist and Bayesian graphical and statistical theory and practice (focusing on R or JAGS interfaced from R). It is advisable that you initially work through the following tutorials sequentially.

This tutorial series makes use of artificial data. The reasons for doing so are:
  • simulating data allows us to fabricate the true underlying trends responsible for the data and therefore enable us to evaluate how accurately the analyses tools subsequently reveal these trends.
  • the process of simulating data is typically the reverse of analysing the data. There must be consideration for how the response is to relate to the predictors, the scale (normal, binomial, Poisson etc) of variables and parameters as well as how to incorporate sensible variability (noise). Thus the process of simulating data specifically for a particular statistical analysis can be as informative as a description of the analysis itself.

Each tutorial is also associated with a workshop featuring a real data sets and research questions (many of which appear in prominent biostatistical texts). These workshops are designed to provide extensive guided practice of the concepts and techniques highlighted in the tutorials. Moreover, as worked examples of real biological data, the provide insights into the diversity of analyses options and challenges presented by real data.

R syntax

Tutorial 1 - R basics
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Topics covered

  1. Installation of R
  2. Basic syntax
  3. Data types
  4. Object manipulation
  5. R Editors
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Tutorial 2 - R Dataframes
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Topics covered

  1. Constructing dataframes
  2. Importing (reading) data
  3. Exporting (writing) data
  4. Vectors within dataframes
  5. Manipulating dataframes
    1. Reshaping dataframes
    2. Merging dataframes
    3. Aggregating dataframes
    4. Transformations and derivatives
    5. Alterations
    6. List manipulations
    7. More complex manipulations
  6. Dummy data sets - random data generation
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Tutorial 3 - More advanced R
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Topics covered

  1. Package management
  2. Matrix algebra
  3. R programming
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R data summaries - numerical and graphical

Tutorial 4 - Exploratory data analysis
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Topics covered

  1. Numerical summaries
  2. Graphical summaries
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Tutorial 5 - Traditional R graphics
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Topics covered

  1. High level plotting
  2. Graphical parameters
  3. Enhancements and customizations
  4. Exporting graphics
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Tutorial 6 - The Grammar of Graphics in R (ggplot2)
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Topics covered

  1. Introducing the grammar of graphics
  2. Coordinate systems
  3. Geometric objects - geom
  4. Scales
  5. Facets
  6. Options
  7. Themes
  8. Showcase
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Linear modeling

Tutorial 7 - Statistical philosophies and estimation
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Topics covered

  1. Distributions
  2. Probability
  3. Opposing philosophies
    1. Frequentist
    2. Bayesian
  4. Estimation and inference
    1. Least squares
    2. Maximum likelihood
    3. Bayesian
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Tutorial 8 - Simple hypothesis testing
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Topics covered

  1. Frequentist philosophy revisited
  2. One and two-tailed tests
  3. t-tests
  4. Assumptions
  5. Power
  6. Robust tests
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Tutorial 9 - Traditional linear modelling in R
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Topics covered

  1. An introduction to linear models
  2. Regression
    1. Simple
    2. Multiple
  3. ANOVA
    1. Single factor
    2. Nested
    3. Factorial
    4. Randomized Complete Block
    5. Partly nested (split-plot and randomized block)
  4. ANCOVA
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Tutorial 10 - The power of contrasts
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Topics covered

  1. To derive predictions
  2. To derive treatment means
  3. To derive effect sizes
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Tutorial 11 - Generalized linear models
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Topics covered

  1. χ2 tests
  2. Contingency tables
  3. Generalized linear models
    1. Logistic regression
    2. Log-linear modelling
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Tutorial 12 - Generalized linear mixed effects
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Topics covered

  1. χ2 tests
  2. Contingency tables
  3. Generalized linear models
    1. Logistic regression
    2. Log-linear modelling
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Multivariate analyses

Tutorial 13 - R-mode analyses
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Topics covered

  1. Principal components analysis
  2. Redundancy analysis
  3. Ordiation
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Tutorial 14 - Q-mode analyses
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Topics covered

  1. Dissimilarity
  2. Classification and regression trees, clustering
  3. Multidimensional scaling
  4. ANOSIM
  5. Mantel
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Other topics

Tutorial 15 - Recommendations and Itemsets
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Topics covered

  1. Itemsets
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