Lectures

• Week 1
  • Understanding variable types in R
  • Understanding the Workspace in R
  • Container Variable Types and Accessing Elements in R
  • Writing and Calling Functions in R
  • Container Variable Type Coercion in R
  • Assignment Operators in R
  • Loops and Conditional Flow in R
  • Getting Help in R
  • Creating a Minimal Reproducible Example
• Week 2
  • Using external libraries
  • Introduction to data.table
  • Adding and updating data.table columns
  • data.table joins
  • data.table reshaping
  • Chaining data.table calls
• Week 3
  • Introduction to ggplot
  • Basic Aesthetics in ggplot
  • Common Geoms in ggplot
  • Scales in ggplot2
  • Customization and Themes in ggplot2
  • Faceting with ggplot2
  • Creating PDFs with R Markdown
• Week 4
  • Visuals Visuals Visuals
  • Core Concepts of Statistical Thinking
  • Descriptive Statistics and Data Visualization in R: Central Tendency
  • Descriptive Statistics and Data Visualization in R: Dispersion and Spread
  • Basic Experiment Design in Psychology and Cognitive Science
• Week 5
  • Random Variables and Probability Distributions
  • Fundamentals of Probability
  • Moments of Random Variables
  • Distributions in R
• Week 6
  • Distribution of Sample Means
  • Central Limit Theorem
  • Normal test
  • Critical Values
  • Two-tailed tests
  • Confidence Intervals
  • Inferential Error and Statistical Power
• Week 7
  • Standardisation and the \(z\) distribution
  • The single sample \(t\)-test
  • \(t\)-tests for comparing two means
• Week 8
  • Effect Size
  • Review of Power
  • P-Hacking
  • Putting it all together
• Week 9
  • One-way ANOVA
  • Repeated Measures One-way ANOVA
  • Two-way ANOVA
  • Mixed ANOVA
• Week 10
  • Correlation
  • Simple linear regression
• Week 11
  • Multiple regression
  • Simple linear regression and multiple regression examples (will be done interactively during lecture)
• Week 12
  • Plain old review session
• Week 13
  • Loose ends
  • Partial correlation