Meet R and RStudio
You have a codebook. You have (or will soon have) a dataset. Now you need a tool to analyze it. That tool is R — a free, open-source programming language built for data analysis and statistics. This chapter gets you set up and comfortable before the real work begins.
Why R?
You might be wondering: Why can’t I just use Excel? You can do some of this in Excel. But R gives you three things Excel doesn’t:
- Reproducibility. Every step of your analysis is written as code. You can re-run it, share it, and verify it. In Excel, clicking through menus leaves no trail.
- Publication-ready output. R + Quarto renders your analysis directly into a professional PDF or website. No copy-pasting charts into Word.
- Statistical power. R handles chi-square tests, effect sizes, and visualizations that would require expensive add-ons in Excel.
You’re not becoming a programmer. You’re learning to use a specific tool for a specific purpose: turning your coded data into findings you can communicate.
The RStudio Interface
When you open RStudio, you’ll see four panels (called “panes”):
┌─────────────────────┬─────────────────────┐
│ │ │
│ Source Editor │ Environment │
│ (where you write │ (your data and │
│ code in .qmd │ variables live │
│ files) │ here) │
│ │ │
├─────────────────────┼─────────────────────┤
│ │ │
│ Console │ Files / Plots / │
│ (where code runs │ Help / Viewer │
│ and output │ (file browser, │
│ appears) │ charts, docs) │
│ │ │
└─────────────────────┴─────────────────────┘- Source Editor (top-left): Where you write and edit your
.qmdfiles. This is where most of your work happens. - Console (bottom-left): Where R actually runs your code. You can type commands here directly, but usually you’ll run code from the Source Editor.
- Environment (top-right): Shows you what data and variables R currently has loaded. When you import a dataset, it appears here.
- Files/Plots/Help (bottom-right): A multi-purpose panel. The Files tab is a file browser. The Plots tab shows your charts. The Help tab shows documentation.
Setting Up Your Environment
Let’s verify everything works. Complete these steps in order — don’t skip ahead.
Step 1: Verify R is Installed
Open RStudio. In the Console (bottom-left), type:
R.version.stringYou should see something like "R version 4.4.2 (2024-10-31)". The exact version doesn’t matter as long as it starts with 4.
Step 2: Install and Load Required Packages
Packages are collections of functions that extend what R can do. You need two:
install.packages("tidyverse")
install.packages("janitor")This may take a few minutes. You’ll see red text scrolling — that’s normal. When it’s done, load them:
library(tidyverse)
library(janitor)If you see no errors, you’re good. If you get an error about a package not being found, try the install command again with dependencies = TRUE:
install.packages("tidyverse", dependencies = TRUE)If R says it can’t install a package because a file is “in use,” close all other R/RStudio windows and try again. Windows sometimes locks files that are open in another session.
Step 3: Create an RStudio Project
Projects keep your files organized and make file paths work correctly.
- In RStudio: File → New Project → New Directory → New Project
- Name it
MC451_Analysis(no spaces) - Choose a location you can find easily (e.g., your Documents folder)
- Click Create Project
RStudio will restart in your new project. You’ll see the project name in the top-right corner of the window.
Step 4: Test with the Music Dataset
Download music_data_raw.csv from Blackboard (or copy it from this workbook’s data/ folder). Place it in your MC451_Analysis project folder.
Now load it:
library(tidyverse)
library(janitor)
music <- read_csv("data/music_data_raw.csv")
glimpse(music)If you see a table of data in the Console and music appears in your Environment pane (top-right), everything is working.
Step 5: Verify the coursepackR Package (Optional)
Your professor has created a course-specific R package with setup tools and diagnostics:
install.packages("remotes")
remotes::install_github("SIM-Lab-SIUE/coursepackR")
library(coursepackR)
mccourse_check()This will verify your entire environment — R, RStudio, Quarto, Git, and all required packages. Fix any issues it flags.
Quarto Documents (.qmd)
All of your R assignments are written in Quarto documents — files ending in .qmd. A Quarto document mixes text and code in a single file. When you “render” it, Quarto runs your R code, captures the output (tables, charts, numbers), and produces a polished PDF or HTML document.
The Structure of a .qmd File
A Quarto document has three parts:
1. YAML Header (at the very top, between --- marks):
---
title: "My Analysis"
author: "Your Name"
format: pdf
---2. Text (written in plain language, just like any document):
This analysis examines the relationship between genre
and musical mode in the Billboard music dataset.3. Code Chunks (R code between special markers):
```{r}
library(tidyverse)
music <- read_csv("data/music_data_raw.csv")
```When you click the Render button (or press Ctrl+Shift+K), Quarto:
- Runs all the R code chunks in order
- Captures their output (tables, charts, printed results)
- Combines the text and output into a finished document
- Saves it as PDF or HTML (whichever you specified)
This is exactly how your final portfolio will work — one Quarto project that renders into both a PDF and a website.
Try It Yourself
- Create a new Quarto document in your project: File → New File → Quarto Document
- Give it a title like “My First Quarto Doc”
- In the first code chunk, type:
library(tidyverse)
music <- read_csv("data/music_data_raw.csv")
nrow(music)- Click Render (or Ctrl+Shift+K)
- You should see a PDF (or HTML) with the number
1792— the number of songs in the dataset
If this works, you’re ready for Chapter 4.
Every assignment from here forward is a .qmd file. Your final portfolio is a Quarto book — a collection of .qmd files that render together. The skills you’re building now (writing text + code in the same document) are the exact skills you’ll use to publish your research.