• Overview of the Course
    • Topics we will cover
    • Learning Outcomes
    • Data sets
    • Schedule
    • Expectations
    • Lets stay in touch
    • Authorship
  • 1 CoopR
    • 1.1 Think algorithmically
    • 1.2 Don’t be scared
  • 2 RStudio
  • 3 Basic principles of R programming
    • 3.1 If you want to keep it, put it in a box
    • 3.2 You can’t really change an object
    • 3.3 It’s elementary, my dear Watson
      • 3.3.1 Data structures
    • 3.4 There are only three ways to ask for elements
      • 3.4.1 Indices
      • 3.4.2 Logical vectors
      • 3.4.3 Complementary subsetting
      • 3.4.4 Names - $ subsetting
    • 3.5 Think of commands in terms of their output
    • 3.6 Know what to expect
  • 4 Putting it all together
    • 4.1 Are there quicker ways?
  • 5 Functions
    • 5.1 Packages
    • 5.2 Using functions
      • 5.2.1 The ()s
      • 5.2.2 Specifying arguments
    • 5.3 Function output
      • 5.3.1 Command is a representation of its output
      • 5.3.2 Knowe thine output as thou knowest thyself
    • 5.4 Output is an object
      • 5.4.1 Where does the output go?
  • 6 Making the computer do the heavy lifting
    • 6.1 Avoiding repetition and avoiding repetition
    • 6.2 The apply() family
    • 6.3 Conditional code evaluation
      • 6.3.1 if
      • 6.3.2 else
      • 6.3.3 else if
      • 6.3.4 ifelse()
      • 6.3.5 Nesting clauses
    • 6.4 Writing your own functions
      • 6.4.1 Functions are objects too!
      • 6.4.2 Anatomy of a function
      • 6.4.3 DIY
  • 7 Reshaping Data
    • 7.1 Tidyverse
    • 7.2 Wide to long
      • 7.2.1 gather()
      • 7.2.2 separate()
      • 7.2.3 select()
    • 7.3 Descriptives in tidyverse
  • 8 Data visualisation
    • 8.1 qplot()
    • 8.2 ggplot()
  • 9 Useful tips
    • 9.1 Break it down
    • 9.2 Handy functions that return logicals
  • 10 Tests and modelling in R
    • 10.1 Hypothesis testing
  • 11 Examining Relationships (more than one variable)
    • 11.1 T test
    • 11.2 Chi squared distribution and test
      • 11.2.1 Contingency tables
    • 11.3 Chi squared distribution
    • 11.4 One way Anova example
  • 12 Lets take a break!
  • 13 Correlation, Causation and LM
    • 13.1 Sharks and ice cream example
    • 13.2 Simple Linear Regression in R
    • 13.3 Regression Diagnostics
      • 13.3.1 Violations of the assumptions: available treatments
    • 13.4 Interaction (simple slope) and multiple explanatory factors
  • 14 Model selection
  • 15 Linear Model and Mixed Methods
    • 15.1 Longitudinal Data
    • 15.2 Why do we do this?
    • 15.3 Ecological Fallacy (quick illustration)
    • 15.4 Simple model
      • 15.4.1 Pooling
      • 15.4.2 No pooling
      • 15.4.3 Partial Pooling (varying intercepts)
      • 15.4.4 Partial Pooling (varying intercepts and/or slopes)
    • 15.5 Multilevel modelling with random intercepts and slopes
      • 15.5.1 Prepare
    • 15.6 Random slopes, intercepts and cross level interactions (optional)
  • 16 High Dimensional Analysis
  • 17 Extra Resources
    • 17.1 More R practice
    • 17.2 Data Cleaning
    • 17.3 Visualisations
    • 17.4 Stuff we did not cover
    • 17.5 Big Data

PhD Training Workshop: Statistics in R

Chapter 17 Extra Resources

17.1 More R practice

Quck R

Data Camp R R and other programmin lanaguges community forum

Coursera Statistics with R

R cheatsheets

Try R

R tutorial

Another good introduction to R

Advanced R

17.2 Data Cleaning

Intrduction to Data Cleaning with R

17.3 Visualisations

Visualisations cheat sheet

Visualisations in R with ggvis

Top 50 visualisation in R

17.4 Stuff we did not cover

Ezanova package for extensive coverage of various types of ANOVA

Logistic models in R

Factor Analysis

Data Reduction methods (PCA)

17.5 Big Data

Clustering(very brief)

Classification with Tree-methods

Introduction to Statistical Learning

Elements of Statistical Learning