Prerequisites and Computational Setup

Assumed Knowledge

We assume you know the following from StatProg1, Stat1 & Stat2:

1. basics of programming in R (scripts, RStudio, installing packages etc.)
2. literate programming with Quarto markdown documents (qmd)
3. tidy data principles and basic data wrangling with dplyr and tidyr (pivoting, missing values, joins)
4. data visualisation with ggplot2, accessibility best practices
5. high level understanding of different data types and data sources (e.g. panel data, time-series, text)
6. introductory statistical methods: descriptive statistics, linear regression, hypothesis testing

Useful sources for revising these topics:

• LMU OSC Introduction to version Control with git and GitHub within RStudio: https://lmu-osc.github.io/Introduction-RStudio-Git-GitHub
• Monash StartR modules: https://startr.numbat.space
• R4DS: https://r4ds.hadley.nz
• ModernDive textbook (v2): https://moderndive.com/v2/
  
• What the forgot to teach you about R: https://rstats.wtf/projects.html

Computational Requirements

In this course we will use:

• R
• RStudio
• Quarto
• Command Line Interface
• git
• GitHub

And numerous R packages including tidyverse, and palmerpenguins.

By the end of Week 1, please ensure you:

1. Install R and Rstudio
2. Can install packages in R:

install.packages("tidyverse")

1. Install Git
2. Set up your GitHub account
3. Install Quarto