Week 1

Contents

  1. Why use R?
  2. Installing R
  3. Getting Help
  4. Customization and Expansion
  5. Basics of Vectors and Assignment
    1. Assignment
    2. Vector Arithmetic
    3. Indexing
    4. Handy Vector Functions
  6. Matrices
    1. Matrix Indexing
    2. Handy Matrix Functions

Week 2

Contents

  1. References
  2. Factors
    1. levels()
    2. relevel()
  3. Converting Types
    1. Converting Characters to...
      1. ...factors
      2. ...numerical
    2. Converting Factors to...
      1. ...character
      2. ...numerical
    3. cut()
  4. Data Input and Output
    1. Input
    2. Output
  5. Data Frames
    1. Data Frame Indexing and Subsetting
    2. More Summary Techniques

Week 3

Contents

  1. Observation Types
    1. Nominal
    2. Ordinal
    3. Interval and Ratio
    4. What really matters
  2. Distributions
    1. Populations and Samples
      1. Defining Variance and Standard Deviation
      2. The Normal Distribution
    2. An Example
      1. Continuous measurements
    3. Normality of Sample Means
    4. What We've Got
    5. Normality Testing
  3. Tests for the mean
    1. t-tests
      1. One Sample t-test
      2. Two Sample t-tests
      3. Paired t-test
    2. Non-parametric tests
      1. One Sample Wilcoxon test
      2. Two Sample Wilcoxon test
      3. Matched Pairs Wilcoxon test

Week 4

Contents

  1. Base Graphics
  2. Plotting Basics
    1. Plot Types
    2. Point Types
    3. Line Types
    4. Weights and Sizes
    5. Colors
      1. Color Brewer
      2. RGB, HSV, HCL
  3. Building up Plots
    1. par()
      1. mfcol, mfrow
      2. mar
      3. bg
    2. Plot Region
      1. x and y limits
      2. Axes
      3. Bounding Box
      4. Titles and Axis Labels
    3. Adding Elements
      1. points()
      2. lines()
      3. segments()
      4. arrows()
      5. text()
      6. abline()
  4. Higher Level Plots
    1. Boxplots
    2. Stripchart
    3. Histogram
    4. Bar Chart
    5. Dot Chart
    6. Pie Charts
    7. Interaction Plot
  5. Legends, Labels, Etc.
    1. locator()
    2. identify()
    3. legend()

Week 5 and 6

Contents

  1. Continuous Data
  2. Correlation
    1. Correlation Matrix
    2. Correlation Significance
    3. Non-parametric Tests
  3. Simple Regression
    1. Confidence and Prediction Lines
  4. Categorical Predictors / ANOVA
    1. ANOVA
    2. Regression and Contrasts
      1. The Lesson
      2. Pairwise Testing
    3. Multiple Regression
    4. Polynomial Regressions and Interactions

Week 7

Contents

  1. Categorical Data
  2. Proportion and Independence Tests
    1. Chi-Square
      1. ING
  3. Logistic Regression
  4. Polynomial Regression
  5. Interactions
    1. -t/d Deletion
    2. Interactions and Polynomial Regression

Useful Links:

R Project

CRAN

RSeek

Crantastic

Stack Overflow - R Questions

Books

Introductory Statistics with R by Peter Dalgaard

Analyzing Linguistic Data: A practical introduction to statistics by Harald Baayen

Quantitative Methods in Linguistics by Keith Johnson

Data Analysis Using Regression and Multilevel/Hierarchical Models by Andrew Gelman and Jennifer Hill

Useful Web Documents

ggplot Book Get it while it's hot. This won't be available free online once the book actually goes to press.

Penn MLM Reading Group

Useful R and Statistics Blogs

Revolutions News about R, statistics and the world of open source from the staff of REvolution Computing
Statistical Modeling, Causal Inference, and Social Science
Twitter R Hastag (#rstats)