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Resource downloads

15 Jul 2017

This page provides links to download many of the resources (datasets, scripts and presentations) used throughout the workshop and tutorial series. The datasets and scripts are organized according to topics that reflect the main contents page. Zipped versions of these resources are also available.

Zipped versions

  1. All datasets (as a zip file)
  2. All R scripts (organized into workshops and tutorials as a zip file)
  3. All Presentations (as a zip file)

Presentations

The tutorial and workshop materials are also accompanied by a set of associated presentations. These presentations are available in two formats:

  • live html (browser based). Note these often include embedded Mathjax and do not render well on Internet Explorer.
  • pdf versions for download and printing.

Course intro

Topic 0 - Course intro
«Download pdf version»
«Download pdf notes version»
«Goto Presentation»
«Goto R script»

Topics covered

  1. Schedule
  2. Preparation
  3. R Editors
  4. Reproducible research
  5. Cheatsheets
«Goto Presentation»
«Goto R script»

R syntax

Topic 1 - R basics
«Download pdf version»
«Download pdf notes version»
«Goto Presentation»
«Goto R script»

Topics covered

  1. Basic syntax
  2. Data types
  3. Object manipulation
  4. R Editors
  5. Package management
  6. Reproducible research
«Goto Presentation»
«Goto R script»

Topic 2 - R Dataframes and data manipulation

Topics covered

  1. Dataframes(PDF, Notes, HTML, R script)
    1. Constructing
    2. Importing (reading) and exporting (writing) data
    3. Vectors in dataframes
  2. Manipulating dataframes(PDF, Notes, HTML, R script)

Topic 3 - More advanced R

Topics covered

  1. Dataframes
  2. Matrix algabra(PDF, HTML, R script)
  3. R programming
  4. Time formatting

Introductory statistical principles

Topic 4 - Basic statistics
«Goto PDF»
«Goto Notes»
«Goto Presentation»
«Goto R script»

Topics covered

  1. Estimation
  2. Probability theory
  3. Frequentist hypothesis testing
  4. Exploratory data analysis
«Goto PDF»
«Goto Presentation»
«Goto R script»

R data summaries - numerical and graphical

Topic 5 - Graphics

Topics covered

  1. Traditional R Graphics(PDF, HTML, R script)
  2. The Grammar of Graphics(PDF, Notes, HTML, R script)
  3. Exploratory data analysis(PDF, HTML, R script)
  4. Mapping in R(PDF, HTML, R script)

Statistical modeling

Topic 6 - Simple hypothesis testing

Topics covered

  1. Frequentist philosophy revisited(PDF, PDF, HTML, R script)
  2. t-tests(PDF, Notes, HTML, R script)

Topic 7 - Linear models

Topics covered

  1. An introduction to linear modelsSlides
  2. Regression
    1. Simple Bayesian (P, N, S, R)   (P, N, S, R)
    2. Multiple Bayesian (P, N, S, R)   (P, N, S, R)
  3. ANOVA
    1. Single factor Bayesian (P, N, S, R)   (P, N, S, R)
    2. ANCOVA Bayesian (P, N, S, R)   (P, N, S, R)
    3. Factorial Bayesian (P, N, S, R)   (P, N, S, R)

Topic 8 - Heterogeneity and autocorrelation

Topics covered

  1. An introduction to variance structures in linear modelsSlides
  2. Dealing with variance heterogeneity Bayesian (P, N, S, R)   (P, N, S, R)
  3. Dealing with temporal and spatial autocorrelation Bayesian (P, N, S, R)   (P, N, S, R)

Topic 9 - Linear mixed effects models

Topics covered

  1. An introduction to mixed effects modelsPDF, Notes, Slides
  2. Nested Bayesian (P, N, S, R)   (P, N, S, R)
  3. Randomize Complete Block Bayesian (P, N, S, R)   (P, N, S, R)
  4. Partly nested (split-plot and randomized block) Bayesian (P, N, S, R)   (P, N, S, R)

Topic 10 - Frequency analyses and generalized linear models

Topics covered

  1. An introduction to frequency analysisTutorial
  2. χ2 tests Bayesian (P, N, S, R)   (P, N, S, R)
  3. Contingency tables Bayesian (P, N, S, R)   (P, N, S, R)
  4. Generalized linear modelsTutorial
    1. Logistic and probit regression Bayesian (P, N, S, R)   (P, N, S, R)
    2. Poisson regression and Log-linear modelling Bayesian (P, N, S, R)   (P, N, S, R)

Topic 11 - Generalized linear mixed models

Topics covered

  1. Generalized linear mixed modelsTutorial
    1. GLMM Bayesian (P, N, S, R)   (P, N, S, R)

Topic 12 - Non-linear models

Topics covered

  1. Polynomial models Bayesian (P, N, S, R)   (P, N, S, R)
  2. Non-linear models Bayesian (P, N, S, R)   (P, N, S, R)
  3. Lowess (loess) regression Bayesian (P, N, S, R)   (P, N, S, R)
  4. Piecewise regression Bayesian (P, N, S, R)   (P, N, S, R)
  5. Splines Bayesian (P, N, S, R)   (P, N, S, R)
  6. Generalized additive models
    1. GAM Bayesian (P, N, S, R)   (P, N, S, R)
    2. GAMM Bayesian (P, N, S, R)   (P, N, S, R)
  7. Classification and regression trees
    1. Simple trees Bayesian (P, N, S, R)   (P, N, S, R)
    2. Boosted regression trees Bayesian (P, N, S, R)   (P, N, S, R)
    3. Multivariate regression trees Bayesian (P, N, S, R)   (P, N, S, R)

Multivariate analyses

Topic 13 - Multivatiate data

Topics covered

  1. Overview Tutorial
  2. Diversity and Richness Tutorial
  3. Transformations and standardizations Tutorial
  4. Measures of association and distance Tutorial

Topic 14 - R-mode analyses

Topics covered

  1. Axis rotation and eigenanalyis Tutorial
  2. Unconstrained
    1. Principal Components Analysis (PCA) (Tutorial, Workshop)
    2. Correspondence Analysis (CA) (Tutorial, Workshop)
  3. Constrained
    1. Redundancy Analysis (RDA) (Tutorial, Workshop)
    2. Canonical Correspondence Analysis (CCA) (Tutorial, Workshop)

Topic 15 - Q-mode analyses

Topics covered

  1. Non-metric Multidimensional Scaling (NMDS) (Tutorial, Workshop)
  2. ANOSIM and Mantel tests (Tutorial, Workshop)
  3. Clustering (Tutorial, Workshop)

I have yet to implement individual file links yet. I guess you will just have to download all of them from above.