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About this site

Getting started with R

• Installing R and RStudio
• Using RStudio notebooks
• Basic project management
• Data entry
• Importing data and data cleaning
• Data types & structure

Data manipulation

• Reshaping data (wide and long formats)
• Subsetting data
• Summarising data
• Combining data sets
• Making new variables

Coding skills

Graphics

• Basic plotting in R
  • Single continuous variable
  • Two continuous variables
  • Single continuous vs categorical variable
• Plotting with ggplot
  • ggplot1 basics
  • ggplot 2 formatting: overall appearance
  • ggplot 2 formatting: titles and axis names
  • ggplot 2 formatting: colours and symbols
  • ggplot: bar plots with error bars
• Visualising multivariate data sets
  • Multi-dimensional scaling
  • Principal components analysis
  • Classification (cluster diagrams)
  • Heat maps
• Visualising spatial data
  • Making maps of your study sites
  • Making simple maps

Statistics

• t tests
  • One sample t test
  • Independent samples t test
  • Paired t test
• Linear models
  • Linear regression
  • Analysis of variance (one-way)
  • Analysis of variance (factorial)
  • Understanding interactions
  • Interpreting coefficients in linear models
• Generalised linear models
  • GLM 1: introduction/binomial data
  • GLM 2: count data
  • Interpreting coefficients in GLMs
• Mixed models
  • Mixed models 1: Linear mixed models with one random effect
  • Mixed models 2: Linear mixed models with several random effects
  • Mixed models 3: Generalised linear mixed models
• Generalised additive models
• Categorical data analyses
  • Goodness of fit tests
  • Contingency tables
• Power analyses
• Introduction to mvabund
• Forecasting with time series
• Meta-analyses
  • Meta-analyses 1: introduction and calculating effect sizes
  • Meta-analyses 2: Fixed effect and random effect models
  • Meta-analyses 3: More complex models