• PPLS Summer Training (R and Stats)
  • Overview of the Course
    • Programme
    • Learning Outcomes
    • Preparation
    • Data sets
    • Expectations
    • Lets stay in touch
    • List of extra resources
    • Authorship
    • Last, but not least
  • 1 Getting Started in RStudio
    • 1.1 R as an interactive envrionment
    • 1.2 Setting Up Your Working Directory
    • 1.3 Spacing
    • 1.4 Typos
    • 1.5 Unfinishe…. d
    • 1.6 Basic Arithmetic
    • 1.7 Using Functions for Calculations
    • 1.8 Short Example
    • 1.9 Numeric Data
    • 1.10 Text/Character Data
    • 1.11 Logical Data
      • 1.11.1 Exercise
    • 1.12 Factors
      • 1.12.1 Exercise
    • 1.13 Practical Example
    • 1.14 Subsetting Dataframes
      • 1.14.1 Replacing Values & NAs
    • 1.15 Indexing Data Frames
    • 1.16 Data Cleaning
    • 1.17 Simple Plots
      • 1.17.1 Using plot()
    • 1.18 Customising Plots
      • 1.18.1 Labels
      • 1.18.2 Plot Type
      • 1.18.3 Other Customisable Features
      • 1.18.4 Change Axes
    • 1.19 Don’t Panic!
    • 1.20 Other Simple Plots
      • 1.20.1 Histograms
      • 1.20.2 Boxplots
      • 1.20.3 Scatterplots
    • 1.21 How to Save Image Files
    • 1.22 Plotting with ggplot2
      • 1.22.1 ggplot()
      • 1.22.2 Iris Example
      • 1.22.3 ggplot() with our mydata file
    • 1.23 Bring It All Together
    • 1.24 Write Out Files
    • 1.25 Questions
  • 2 Tests and modelling in R
    • 2.1 Hypothesis testing
    • 2.2 T test
    • 2.3 Chi squared distribution and test
      • 2.3.1 Contingency tables
    • 2.4 Chi squared distribution
    • 2.5 One way Anova
    • 2.6 Sharks and ice cream example
    • 2.7 Simple Linear Regression in R
    • 2.8 Regression Diagnostics - assess the validity of a model
      • 2.8.1 Violations of the assumptions: available treatments
    • 2.9 Standardisation
    • 2.10 Interaction (simple slope) and multiple explanatory factors
    • 2.11 AIC & BIC
  • 3 Simple Linear Model and Mixed Methods
    • 3.1 Data sets
    • 3.2 Longitudinal Data
    • 3.3 Why a new model?
    • 3.4 Ecological Fallacy (quick illustration) - no need to run
    • 3.5 Simple Example
    • 3.6 Now for Advanced: Model set up
      • 3.6.1 Pooling
      • 3.6.2 No pooling
      • 3.6.3 Partial Pooling (varying intercepts)
      • 3.6.4 Partial Pooling Extended - (varying intercepts and/or slopes)
    • 3.7 Multilevel modelling with random intercepts and slopes
      • 3.7.1 Overview of the data set
      • 3.7.2 Prepare
    • 3.8 Random slopes, intercepts and cross level interactions (optional)
    • 3.9 Simple Example
      • 3.9.1 Optional (Odds Refresher)
    • 3.10 Data Description
  • 4 Introduction to Bayesian Estimation
    • 4.1 Intro to Bayesian estimation
      • 4.1.1 Data sets
    • 4.2 Bayes inference and one-sample t-test
    • 4.3 Difference between two groups’ means
    • 4.4 Bayes Factor Example
    • 4.5 Bayes Factor and Anova
      • 4.5.1 Exercise
    • 4.6 Linear models with BAS
      • 4.6.1 BIC and R squared
    • 4.7 Predictions from bas.lm
    • 4.8 Examining and presenting results
    • 4.9 Bayesian Mixed methods example (Optional)
      • 4.9.1 Data
    • 4.10 Extra Resources to check
  • 5 Extra Resources
    • 5.1 Resources for Rmarkdown and Twitter workshop
    • 5.2 More R practice
    • 5.3 Data Cleaning
    • 5.4 Visualisations
    • 5.5 Other Common Methods in R
    • 5.6 Big Data
  • Published with bookdown

PPLS PhD Training Workshop: Statistics and R

Chapter 5 Extra Resources

5.1 Resources for Rmarkdown and Twitter workshop

Introduction to R Markdown

Sentiment Analysis usng tidytext

R Markdown file - Analsying Twitter Data with R

5.2 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

5.3 Data Cleaning

Intrduction to Data Cleaning with R

5.4 Visualisations

Visualisations cheat sheet

Visualisations in R with ggvis

Top 50 visualisation in R

5.5 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)

5.6 Big Data

Clustering(very brief)

Classification with Tree-methods

Introduction to Statistical Learning

Elements of Statistical Learning