Learning goals

• Understand the fundamentals of working with R
• Learn how to organize your R project and workflow
• Create and render your first quarto notebook
• Take on board some R basics

Fundamentals

• Use R with RStudio
• Create an RStudio project for each new research project/study
• Use notebooks (Quarto documents), which include R code + output
• Project folder: Organization into subfolders (data, figures, etc.)
• Everything should always be reproducible from code + input data

Create an RStudio project

• File : New project…
• New Directory
• New Project
• Use an informative directory name
• Store it where you will find it

RStudio project organization

• A project should be self-contained: All in one place
• Keep data files and .qmd-notebooks in your project folder
• Use subfolders: data, figures

RStudio project setup

• Go to your project folder
• Create two subfolders: data, figures

RStudio project setup

This is what a minimal R project setup looks like:

Use notebooks (Quarto documents)

• Quarto documents combine text, code, and output
• Can be exported as html/Word/PDF file
• In the analysis notebook:
  • Talk to your (future) self: Say what you do (and why you do it)
  • Run analyses
  • Look at output (figures, tables,…)

Create a Quarto document

• File : New file : Quarto document…
• Use default options, click “Create”
• Save it in your project folder
• Render it: Click the Render button

Play around with the Quarto file

• Compare contents of file with rendered version
• Change/add text and render again
• Add headers / sub-headers
• Run code inside of RMarkdown file
• Add bold print **bold**
• Add italics *italics*
• Have a look at the RMarkdown Cheat Sheet

R: General advice

• Use the tidyverse set of packages
• Use ggplot2 for data visualization (Session 2)
• Workflow: Document every step using code
• Aim: Full reproducibility based on code + data

R packages

• For some things, you need packages (sets of functions)
• You need to install a package only once: install.packages()

install.packages("tidyverse")

• Packages must be loaded at beginning of R session: library()

library(tidyverse)

• Notebooks should start with section ## R setup, where you load the packages you need

In R, everything is an object

• Functions, data tables, etc.
• Create an object by assigning something to a new name
• Assignment operator: <- (shortcut: Alt + -)
• New objects then exist in R’s workspace (top right panel)
• Important object types:
  • Vector: Sequence of elements (e.g. names, numbers)
  • Data frame/tibble: Data tables

In R, you do things using functions

• Functions do the work in R (calculation, graphing, etc.)
• Functions have parentheses at the end of their names
• We give instructions to a function using its arguments
• function(argument = "...")

Some useful functions

• Inspect contents of data frame: str()
• Inspect first few rows of data frame: head()
• mean(), median()
• Access columns (= variables) in a data frame: $