ECON 465 Week 1 Lab Handout: Setting Up R, RStudio, and Quarto

Reproducible Economic Research Setup + First Exercises

Author

Instructor: Gül Ertan Özgüzer

1 Before You Start

1.1 Lab Goal

This lab is designed for self-installation.
Please complete all steps before class and come prepared to run the exercises.

This is your first step toward becoming a data-driven economist.

1.2 Lab Objectives

By the end of this lab, you will be able to:

  • Install and configure R, RStudio, and Quarto
  • Create a structured RStudio Project
  • Write and execute basic R commands
  • Use essential keyboard shortcuts
  • Install and load R packages
  • Create, edit, and render a Quarto document

1.3 Pre-Lab Checklist

You will need:

  • A laptop with internet access
  • 30–45 minutes of uninterrupted time

2 Install the Software (30 minutes)

We will use the standard toolkit of modern economic research:

  • R → statistical programming language
  • RStudio → integrated development environment
  • Quarto → reproducible publishing system

2.1 Install R

  1. Go to: https://cran.r-project.org

  2. Select your operating system:

    • Windows → Download R for Windows → base
    • macOS → Download R for macOS (choose correct chip version)
    • Linux → Follow distribution-specific instructions

  3. Install using default settings.

2.1.1 Verification

Open R once.
If you see a console window with > prompt, installation is successful.

2.2 Install RStudio Desktop

  1. Go to: https://posit.co/download/rstudio-desktop/
  2. Download RStudio Desktop (Free)
  3. Install using default settings.

2.2.1 Verification

Open RStudio.

You should see:

  • Console (bottom-left)
  • Environment/History (top-right)
  • Files/Plots/Packages/Help (bottom-right)

2.3 Install Quarto

Quarto is the publishing engine that allows us to combine:

  • Text
  • Code
  • Output

into a single reproducible document.

You do not need to manually download anything from quarto.org during this lab.

2.3.1 Step 1 — Install Required R Packages

Open RStudio and run the following in the Console:

install.packages(c("rmarkdown", "quarto"))

These packages enable document rendering inside R.

2.3.2 Step 2 — Verify Quarto Installation

After installation, restart RStudio.

Then in the Console, run:

quarto::quarto_version()
[1] '1.8.25'

If Quarto is properly available, you should see a version number printed.

2.3.3 Step 3 — Confirm Rendering Works

In RStudio:

File → New File → Quarto Document

If this option appears in the menu, your installation is complete.

2.3.4 Why This Matters

Quarto ensures:

  • Reproducibility
  • Transparent research workflow
  • Professional-quality reports

In this course, all assignments will be submitted as Quarto documents.

3 Create Your First Reproducible Project

A reproducible workflow means:

Your future self (and your instructor) can run your project and obtain the same results.

In this course, every analysis must live inside a structured project folder.

3.1 Create an RStudio Project

In RStudio:

File → New Project → New Directory → New Project

  • Directory name: econ_465_week2
  • Choose a location
  • Leave Create a git repository unchecked (we will learn Git later)
  • Click Create Project

3.1.1 Verification

You should see the project name displayed in the top-right corner of RStudio.

You are now “inside” your project.

3.2 Create a Clean Folder Structure

In the Files pane (bottom-right), click New Folder and create:

  • data/
  • scripts/
  • reports/
  • figures/

3.2.1 Why this matters

Professional data scientists separate:

  • raw data
  • analysis scripts
  • reports
  • outputs

Clean structure prevents confusion and ensures reproducibility.

4 R Basics

Before we analyze economic data, we must understand how R works.

4.1 Console vs Script

  • The Console runs commands immediately (temporary use).
  • A Script saves your code permanently (reproducible research).

4.1.1 Step 1 — Create Your First Script

In RStudio:

File → New File → R Script

Save it as:

scripts/week1_basics.R

4.1.2 Step 2 — Copy and Run the Following Code

# Basic arithmetic
2 + 2
[1] 4
10 / 4
[1] 2.5
# Creating objects (variables)
x <- 10
y <- 3
x + y
[1] 13
x / y
[1] 3.333333
# A numeric vector (example: GDP growth rates)
gdp_growth <- c(4.7, 5.6, -1.8, 3.2)

# Calculate the mean growth rate
mean(gdp_growth)
[1] 2.925

4.1.3 Verification

You should see numeric outputs appear in the Console.

If nothing happens, check that you pressed:

  • Ctrl + Enter (Windows)
  • Cmd + Enter (Mac)

4.2 Essential Key Bindings

Using shortcuts will save you hours this semester.

4.2.1 Run current line or selection

  • Windows: Ctrl + Enter
  • macOS: Cmd + Enter

4.2.2 Insert pipe operator (|> or %>%)

  • Windows: Ctrl + Shift + M
  • macOS: Cmd + Shift + M

4.2.3 Comment or uncomment lines

  • Windows: Ctrl + Shift + C
  • macOS: Cmd + Shift + C

4.2.4 Small Practice Exercise

Add the following to your script and run it:

inflation <- c(8.2, 10.5, 6.1, 12.3)
mean(inflation)
[1] 9.275
sd(inflation)
[1] 2.70108

Ask yourself:

  • What does mean() compute?
  • What does sd() represent economically?

You are now ready to move from simple calculations to real economic datasets.

5 Packages: Install Once, Load Often

R is powerful because of its packages — collections of functions written by other researchers and developers.

In this course, we will use packages for:

  • Data wrangling
  • Visualization
  • Modeling
  • Reproducible reporting

5.1 Install Required Packages

You only need to install a package once.

Run the following in the Console:

install.packages(c(
  "tidyverse",
  "tidymodels",
  "janitor",
  "skimr",
  "palmerpenguins"
))

5.1.1 What these packages do

  • tidyverse → data manipulation & visualization
  • tidymodels → machine learning framework
  • janitor → clean variable names
  • skimr → quick data summaries
  • palmerpenguins → example dataset for practice

If installation fails, check your internet connection and try again.

5.2 Task 4.2 — Load the Packages

After installation, load them in your script:

library(tidyverse)
library(tidymodels)
library(janitor)
library(skimr)
library(palmerpenguins)

5.2.1 Verification

If no red error messages appear, the packages loaded successfully.

If you see: > “there is no package called …”

Re-run the installation step.

5.3 Quick Practice

Test whether everything works:

penguins |> 
  glimpse()
Rows: 344
Columns: 8
$ species           <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
$ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
$ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
$ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
$ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
$ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
$ sex               <fct> male, female, female, NA, female, male, female, male…
$ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…

You should see a structured summary of the dataset.

This confirms that:

  • R is working
  • Packages are installed
  • You are ready for analysis

6 What Is a Quarto Document?

A Quarto document (.qmd) combines:

  • Written explanation (Markdown text)
  • R code chunks
  • Output (tables, figures, results)

When you click Render, Quarto:

  1. Runs all R code
  2. Captures the output
  3. Produces a clean HTML (or PDF) document

This ensures:

  • Transparency
  • Reproducibility
  • Professional presentation

6.1 Anatomy of a Quarto File

A Quarto file has three main parts:

6.1.1 1. YAML Header (at the top)

Contains metadata like:

  • Title
  • Author
  • Output format

Example:

---
title: "My Analysis"
author: "Your Name"
format: html
---

6.1.2 2. Text (Markdown)

Normal writing like this:

  • You can create headings using #
  • Bold with **text**
  • Lists with -

6.1.3 3. Code Chunks

R code must be placed inside code chunks:

2 + 2
[1] 4

When rendered, the code runs and prints the result.

6.2 Why Quarto Matters in Economics

In modern economic research:

  • Analysis must be reproducible
  • Code and results must be transparent
  • Reports must be clear and professional

Quarto allows you to:

  • Combine narrative and evidence
  • Avoid copy–paste errors
  • Share work publicly (e.g., RPubs)

6.3 Reflection Question

Why is reproducibility especially important in economics?

Write 2–3 sentences answering this question in your Quarto document.

7 Create and Render Your First Quarto Document

7.1 Create the File

In RStudio:

File → New File → Quarto Document…

  • Title: Week 1: First Quarto Report
  • Author: Your Name
  • Format: HTML

Save the file inside the reports/ folder as:

week2_report.qmd

7.2 Add Setup Chunk

Paste the following directly below your YAML header:

7.2.1 What this does

  • label: setup names the chunk.
  • include: false runs the code but hides it in the final report.
  • We load required packages once at the beginning.

7.3 Add Your First Output

Below the setup chunk, add:

penguins |> 
  count(species)
# A tibble: 3 × 2
  species       n
  <fct>     <int>
1 Adelie      152
2 Chinstrap    68
3 Gentoo      124

7.3.1 What this does

  • Counts observations by species.
  • Produces a clean summary table.

7.4 Add a Plot

Now add:

penguins |> 
  ggplot(aes(x = bill_length_mm)) +
  geom_histogram(binwidth = 2) +
  labs(
    title = "Distribution of Bill Length",
    x = "Bill length (mm)",
    y = "Count"
  )

7.4.1 What this does

  • Creates a histogram of bill length.
  • Adds clear axis labels and a title.

7.5 Render Your Document

Click the Render button at the top of the editor.

Quarto will:

  1. Run all code
  2. Generate an HTML file
  3. Open it in the Viewer pane

7.5.1 Verification

You should see:

  • A table with species counts
  • A histogram
  • Your title and name at the top

If you see errors:

  • Check that packages were installed
  • Confirm the setup chunk appears below YAML
  • Re-run library() commands

8 Publish to RPubs

Once your document renders successfully:

  1. Click Publish (top-right of the Viewer pane)
  2. Choose RPubs
  3. Log in (create account if necessary)
  4. Confirm publish

After publishing:

  • Copy the RPubs link
  • Save the link for submission

8.1 Final Check Before Submission

Make sure:

  • The document renders without errors
  • Your name appears at the top
  • The table and histogram are visible
  • You can open your RPubs link in a browser

You have now created and published your first reproducible economic analysis.

This week, we begin working with real macroeconomic data.