Welcome to the MAST30034 R Repo

• Author: Yue You
• Tutorial Up-to-Date as of: 2021
• Usage: For MAST30034 students only ...

This repository will house all R workshops.

The python stream is available here.

Dates and Times

On Campus:

• Monday 13:15 - 15:15 (R - Yue)
• Tuesday: 14:15 - 16:15 (Python - Akira)
• Wednesday: 11:00 - 13:00 (Python - Akira)
• Thursday: 10:00 - 12:00 (Python - Calvin)

Online:

• Tuesday: 16:15 - 18:15 (R - Yue)
• Wednesday: 14:15 - 16:15 (Python - Calvin)
• Thursday: 13:00 - 15:00 (Python - Akira), 15:15 - 17:15 (Python - Akira)

Tutorials

The first few tutorials will have content, with the remainder of the semester treated as consultations or additional tutorials as outlined:

1. Introduction and Project 1 Overview:

   • Using the JupyterHub server
   • Using GitHub Desktop vs Git CLI (Command Line Interface)
   • Project 1 Overview
   • R Revision
   • Data Serialization
   • Downloading Files using R
   • Advanced: spark Installation

2. Geospatial Visualization and Analysis:

   • HexBins (vs SquareBins), Choropleths.
   • Descriptive statistics
   • Advanced: sparklyr data analysis

3. Regression and Discussion:

   • Linear Regression
   • MSE vs R-Squared
   • Penalized Regression (LASSO and Ridge)
   • Generalized Linear Model example (Poisson for count data)
   • Advanced: sparklyr modeling

4. Machine Learning and Working as a Team:

   • Discussion: Overfitting, Curse of Dimensionality, Feature Engineering, etc.
   • Dimensionality Reduction
   • Agile Methodology + Standups

5. Project 2 Overview

   • Introduction of themes
   • Getting into teams
   • Assessment Overview

Project 2 Tutorials (Week 6 - 12)

• Attendance is mandatory. Groups are excused one absence only.
• The last 2 weeks of tutorials will be Presentations, all groups must attend a designated tutorial.
• The remainder of tutorials will act as checkpoints, consultation, and a chance for your group to conduct standups at a fixed time slot.

R Libraries Covered

Statistical Modeling / Machine Learning:

• glmnet

Data Engineering / End-to-End Pipelines:

• dplyr, sparklyr

Visualizations:

• ggplot2, pheatmap,corrplot, ggmap, tmap

.....