• PPLS Summer Training (R and Stats)
  • Overview
    • Install R
      • Installation Steps
    • Data sets
    • Expectations
    • List of extra resources
    • Authorship
    • Any questions? Get in touch
    • Last, but not least
  • 1 Getting Started in RStudio
    • 1.1 R as an interactive envrionment
    • 1.2 Setting Up Your Working Directory
  • 2 The Console
    • 2.1 Spacing
    • 2.2 Typos
    • 2.3 Unfinishe…. d
    • 2.4 Basic Arithmetic
    • 2.5 Using Functions for Calculations
  • 3 R Scripts
    • 3.1 Short Example
  • 4 Naming variables
  • 5 Vectors
    • 5.1 Numeric Data
    • 5.2 Text/Character Data
    • 5.3 Logical Data
      • 5.3.1 Exercise
  • 6 Variable Classes
    • 6.1 Factors
      • 6.1.1 Exercise
  • 7 Lists
  • 8 Matrices
  • 9 Data Frames
  • 10 Loading Data
    • 10.1 Practical Example
    • 10.2 Subsetting Dataframes
      • 10.2.1 Replacing Values & NAs
    • 10.3 Indexing Data Frames
  • 11 Packages
  • 12 Summary Statistics
    • 12.1 Data Cleaning
  • 13 Visualisations
    • 13.1 Simple Plots
      • 13.1.1 Using plot()
    • 13.2 Customising Plots
      • 13.2.1 Labels
      • 13.2.2 Plot Type
      • 13.2.3 Other Customisable Features
      • 13.2.4 Change Axes
    • 13.3 Don’t Panic!
    • 13.4 Other Simple Plots
      • 13.4.1 Histograms
      • 13.4.2 Boxplots
      • 13.4.3 Scatterplots
    • 13.5 How to Save Image Files
    • 13.6 Plotting with ggplot2
      • 13.6.1 ggplot()
      • 13.6.2 Iris Example
      • 13.6.3 ggplot() with our mydata file
    • 13.7 Bring It All Together
    • 13.8 Write Out Files
    • 13.9 Questions
  • 14 DIY
  • 15 Tests and modelling in R
    • 15.1 Hypothesis testing
  • 16 Examining Relationships (more than one variable)
    • 16.1 T test
    • 16.2 Chi squared distribution and test
      • 16.2.1 Contingency tables
    • 16.3 Chi squared distribution
    • 16.4 One way Anova
  • 17 Correlation, Causation, and LM
    • 17.1 Sharks and ice cream example
    • 17.2 Simple Linear Regression in R
    • 17.3 Regression Diagnostics - assess the validity of a model
      • 17.3.1 Violations of the assumptions: available treatments
    • 17.4 Standardisation
    • 17.5 Interaction (simple slope) and multiple explanatory factors
  • 18 Model selection
    • 18.1 AIC & BIC
  • 19 DIY
  • 20 Extra Resources
    • 20.1 More R practice
    • 20.2 Data Cleaning
    • 20.3 Visualisations
    • 20.4 Other Common Methods in R
    • 20.5 Big Data
  • Published with bookdown

Introductory Resources: Statistics and R

Chapter 20 Extra Resources

20.1 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

20.2 Data Cleaning

Introduction to Data Cleaning with R

20.3 Visualisations

Visualisations cheat sheet

Visualisations in R with ggvis

Top 50 visualisation in R

20.4 Other Common Methods in R

Ezanova package for extensive coverage of various types of ANOVA

Logistic models in R

Factor Analysis

Data Reduction methods (PCA)

20.5 Big Data

Clustering(very brief)

Classification with Tree-methods

Introduction to Statistical Learning

Elements of Statistical Learning