Lesson 2 Review: R Workflow, Quarto, and Core Functions

Purpose of this review lesson

This lesson reviews and reinforces the foundational material from Lessons 1 and 2. Before moving on to visualization and data wrangling, it is important that you understand:

  • how R scripts differ from Quarto documents
  • how RStudio, Quarto, code chunks, and inline code fit together
  • the core workflow expected in this course
  • essential R functions you will use repeatedly

This lesson is designed as a reference you can return to throughout the semester.

R workflow overview

In this course, your workflow follows this pattern:

  1. Write code in R scripts or Quarto documents
  2. Use R to create objects, summaries, figures, and models
  3. Use Quarto to combine analysis and writing
  4. Render documents to produce final reports

Understanding when to use a script versus a Quarto document is critical.

R scripts vs Quarto documents

R scripts (.R files)

An R script is a plain text file that contains only R code.

You use R scripts to:

  • write and test code
  • clean and prepare data
  • experiment with analyses
  • run code step by step

R scripts do not include formatted writing, figures with captions, or narrative interpretation.

Example use case: You are cleaning a dataset or testing code before putting results into a report.

Quarto documents (.qmd files)

A Quarto document combines writing and R code in a single file.

You use Quarto documents to:

  • write homework assignments
  • create reports and manuscripts
  • integrate code, figures, tables, and interpretation
  • produce reproducible outputs

A Quarto document includes: - a YAML header
- written text (Markdown)
- R code chunks
- inline R code

In this course, all submitted work must be written in Quarto unless otherwise stated.

Key difference (important)

  • Use R scripts to develop and test code
  • Use Quarto documents to present and submit work

Most students will use both: Write code in scripts → move finalized code into Quarto.

Review: Week 1 concepts (R basics)

From Week 1, you should understand:

RStudio interface

  • Source: where scripts and Quarto documents are written
  • Console: where R runs code
  • Environment: where objects are stored
  • Files/Plots/Help: where outputs appear

You should primarily work in the Source pane, not the Console.

Objects and assignment

You create objects using <-.

x <- 10
y <- 5

Objects store values so you can reuse them.

Vectors

Vectors store multiple values of the same type.

ages <- c(23, 35, 41, 29, 50)

Common functions for vectors:

mean(ages)
[1] 35.6
median(ages)
[1] 35
sd(ages)
[1] 10.47855
length(ages)
[1] 5

Functions

Functions take inputs and return outputs.

mean(ages)
[1] 35.6

Understanding how to use functions is more important than memorizing them.

Packages

Packages extend R.

You install once:

# install.packages("tidyverse")

You load every session:

library(tidyverse)

Review: Week 2 concepts (Quarto & reproducibility)

YAML header

Every Quarto document begins with a YAML header.

---
title: "My Document"
format: html
---

The YAML controls document settings.

Markdown

Markdown formats your writing using simple syntax.

You use it for: - headings
- lists
- emphasis
- document structure

Markdown keeps reports readable and professional.

Code chunks

R code is written inside code chunks.

2 + 2
[1] 4

Code chunks run when you render the document.

Rendering

Rendering: - runs all code chunks
- generates the final document
- updates all figures and inline values

Always render before submitting.

Inline R code

Inline code inserts R results into sentences.

ages <- c(23, 35, 41, 29, 50)

The sample included 5 participants, and the mean age was 35.6 years.

Inline code prevents copy-and-paste errors and is required for reporting values in this course.

Go-to R functions (bookmark this section)

These are functions you will use throughout the semester.

Basic R functions

Purpose Function
Create vector c()
Mean mean()
Median median()
Standard deviation sd()
Sample size length()
Summary summary()
View data head()

Data wrangling (tidyverse)

Task Function
Select columns select()
Filter rows filter()
Create variables mutate()
Sort data arrange()
Group data group_by()
Summarize data summarise()

Data reshaping

Task Function
Wide → long pivot_longer()
Long → wide pivot_wider()

Visualization (preview)

Task Function
Create plot ggplot()
Scatterplot geom_point()
Line plot geom_line()
Bar chart geom_col()
Labels labs()

Modeling (coming soon)

Task Function
Linear model lm()
Logistic model glm()
Model summary summary()
Confidence intervals confint()

Reproducibility expectations (review)

In this course:

  • analysis and writing live together in Quarto
  • numbers should be generated by R, not typed
  • scripts and documents should be organized
  • work should render without errors

Reproducibility is a professional skill, not just a technical one.

Looking ahead

Next week, you will begin data visualization with ggplot2, where you will apply everything you learned about Quarto, reproducibility, and tidy workflows.

This review lesson is your foundation — keep it bookmarked.