Basics of R and RStudio

EE BIOL C177/C234

Chuliang Song

Today’s Menu 🎯

1. Getting set up with R and RStudio
2. First steps with basic R syntax
3. Using AI as your coding assistant
4. Managing and installing packages
5. Meeting the tidyverse 🌍

Getting Set Up

Installing R and RStudio

Follow this installation guide to get started.

You need two things:

1. R — the engine 🏎️
2. RStudio — the dashboard 🖥️

Why RStudio?

R alone = powerful but clunky

RStudio = a sleek interface that makes everything easier

• Write & organize code
• Visualize results instantly
• Manage files and packages

For this course, RStudio is our primary IDE.

Other IDEs Worth Knowing

• VS Code 🔗 code.visualstudio.com
  • General-purpose, huge extension ecosystem
  • Best AI integration (Copilot, Cursor, etc.)
• Positron 🔗 positron.posit.co
  • Built by the same team behind RStudio
  • A bit in-between — not as R-native as RStudio, not as AI-integrated as VS Code
• Antigravity 🔗 antigravity.google
  • A new IDE from Google (a customized version of VS Code)
  • Native AI integration with Gemini and Claude
  • We will use it for agentic AI tasks towards the end of the course
• All three are fine for this course, but RStudio is our default

Making Your Code Prettier

Two quick wins:

1. Fira Code font — ligatures make <-, |>, != beautiful
   • RStudio setup · Download
2. styler package — auto-format to tidyverse style (link)

# install.packages("styler")
# Then use the Addin in RStudio → "Style active file"

First Steps in R

Hello, World! 👋

The classic first program:

R as a calculator:

Storing Results in Variables

Why <- instead of =?

• <- is for assignment (“put this value in that box”)
• = is for function arguments (mean(x = c(1,2,3)))
• Historical: early stats keyboards had a dedicated ← key!

Exercise

Create y = 5 × 3, then z = y / 2:

Using AI 🤖

AI as Your R Assistant

Don’t memorize every function — let AI help!

• GitHub Copilot works in RStudio (setup guide)

But always verify AI suggestions:

Installing Packages 📦

Standard Approach

Install once, load every session:

Quirk Alert 🤔

install.packages("ggplot2") — quotes

library(ggplot2) — no quotes

Yes, it’s inconsistent. Welcome to R.

pak Method (Recommended) ⭐

# install.packages("pak")    # one-time setup
pak::pak("ggplot2")           # from CRAN
pak::pak("cttobin/ggthemr")   # from GitHub
pak::pak(c("dplyr", "tidyr")) # multiple at once

Why pak?

• ✅ Skips already-installed packages
• ✅ Works with CRAN, GitHub, Bioconductor
• ✅ Clear dependency reporting

We’ll use pak throughout this course.

The :: Operator

pak::pak("ggplot2")

pak::pak() means: “use the pak function from the pak package”

This lets you call a function without loading the whole package — avoiding name conflicts (a huge source of hidden bugs!).

The Tidyverse 🌍

Meet the Tidyverse

The tidyverse is an opinionated collection of R packages designed for data science.

What’s Inside?

Nine packages loaded at once:

Package	Purpose
ggplot2	Data visualization
dplyr	Data manipulation
tidyr	Data tidying
readr	Reading data
purrr	Functional programming
tibble	Modern data frames
stringr	String manipulation
forcats	Factor handling
lubridate	Date/time handling

Handling Conflicts ⚠️

The filter and lag conflicts can cause silent bugs:

library(conflicted)
library(tidyverse)
conflict_prefer("filter", "dplyr")
conflict_prefer("lag", "dplyr")

Or use the explicit package::function() syntax:

dplyr::filter(data, x > 5)

Want to avoid the annoying messages?

You can use the suppressPackageStartupMessages() function to hide the start-up output when loading packages:

suppressPackageStartupMessages(library(tidyverse))

Summary

What We Learned Today

1. ✅ Install R + RStudio (or Positron)
2. ✅ R basics: print(), arithmetic, <-
3. ✅ AI tools + ? for help
4. ✅ Install packages with pak
5. ✅ Load the tidyverse

Next up: Data structures — vectors and tibbles! 🐧