Lesson 1: Getting Started with R and RStudio

First Things First

This course is designed to help you become familiar with R and RStudio, you’re not expected to become a professional R programmer at the end of it. There is a learning curve and there will be moments where you might want to give up (or throw your computer), but I encourage you to stick with it. One day, after practicing and potentially crying about it, it will click and you will be so happy you stuck with it. I believe in you.

Important Resources

I’ve designed this course using free online resources - since R, itself, is a free program. You’re going to want to bookmark the following resources as you will definitely be using these for the duration of this course (and possible beyond):

Learning Goals

By the end of this week, you should be able to:

  • Open RStudio and identify the main panes.
  • Run basic R commands in the Console and from a script.
  • Load and use the tidyverse package.
  • Describe how R and reproducible workflows support public health research.

Readings

  • ModernDive, Chapter 1: Introduction to Data Science
  • OpenIntro Biostatistics, Chapter 1: Introduction to Data

Topics

  • What is data science in the context of public health?
  • R and RStudio basics
  • R scripts vs Console
  • Loading packages (tidyverse)
  • Good habits for saving and organizing work

What is Data Science?

Data science is an interdisciplinary field that focuses on:

  • collecting data,
  • cleaning and managing data,
  • analyzing data,
  • and communicating insights to support decision-making.

It combines skills from:

  • statistics,
  • computer programming,
  • data visualization,
  • and domain knowledge.

In public health, data science is used to turn complex health data into actionable evidence.

Why data science matters in public health.

Public health decisions affect entire populations. Data science allows public health professionals to:

  • identify health disparities,
  • monitor disease trends,
  • evaluate interventions,
  • and allocate resources effectively.

Without data science, public health decisions would rely more heavily on anecdotal evidence rather than systematic analysis.

Types of data used in public health data science

Public health data come from many sources, including:

  • surveillance systems,
  • surveys and questionnaires,
  • electronic health records,
  • clinical trials,
  • administrative and claims data,
  • environmental and geographic data.

Data science methods help integrate these data sources into a coherent analytical framework.

Key applications of data science in public health

Disease surveillance and outbreak detection

Data science helps:

  • detect unusual patterns in disease incidence,
  • identify emerging outbreaks,
  • track disease spread over time and across locations.

Timely analysis can inform rapid public health responses.

Health disparities and equity

Data science allows researchers to:

  • examine differences in health outcomes across populations,
  • identify structural and social determinants of health,
  • quantify inequities by race, ethnicity, income, geography, and language.

These analyses support equity-focused public health interventions.

Program evaluation and policy analysis

Public health programs and policies must be evaluated to determine whether they work.

Data science supports:

  • impact evaluation,
  • cost-effectiveness analysis,
  • longitudinal and causal modeling,
  • evidence-based policy making.

Predictive modeling and risk assessment

Using historical data, data science can:

  • predict disease risk,
  • identify high-risk populations,
  • support prevention and early intervention strategies.

These models complement traditional epidemiologic approaches.

The role of R in public health data science

R is widely used in public health because it:

  • supports statistical modeling,
  • handles large and complex datasets,
  • creates publication-quality visualizations,
  • enables reproducible research through scripts and Quarto documents.

Learning R allows public health professionals to implement data science workflows transparently and efficiently.

Reproducibility and transparency

Reproducibility is a core principle of both data science and public health.

Best practices include:

  • writing clean, well-documented code,
  • using scripts and Quarto documents,
  • tracking data processing steps,
  • rerunning analyses from raw data to final results.

Reproducible workflows increase trust in public health findings.

R and RStudio Basics

What is R?

R is a programming language used for:

  • data analysis and statistics,
  • data visualization,
  • reproducible research,
  • and reporting results.

R itself runs in the background. It does the actual computation, stores objects in memory, and executes your code.

What is RStudio?

RStudio is an integrated development environment (IDE) for R.

Think of R as the engine of a car, and RStudio as the dashboard. RStudio makes it easier to:

  • write and organize code,
  • run analyses,
  • view plots and data,
  • manage files and projects,
  • and create Quarto documents.

You can use R without RStudio, but almost everyone uses R through RStudio because it is more efficient and user friendly.

R vs RStudio (key distinction)

  • R does the computing
  • RStudio helps you interact with R
Warning

If R is not installed, RStudio will not work.

The RStudio interface

When you open RStudio, you typically see four panes.

Source pane (top-left)

This is where you:

  • write R scripts (.R),
  • write Quarto documents (.qmd),
  • edit code without running it immediately.

Nothing in this pane runs until you tell R to run it.

Console pane (bottom-left)

The Console is where:

  • R actually runs code,
  • results and error messages appear,
  • commands are executed line by line.

You can type code directly here, but for reproducibility, most code should live in scripts or Quarto files.

Environment / History pane (top-right)

The Environment tab shows:

  • objects currently loaded in memory,
  • data frames, vectors, functions, and values.

If you restart R, this pane clears because the session memory resets.

Files / Plots / Packages / Help pane (bottom-right)

This pane lets you:

  • browse project files,
  • view plots,
  • manage installed packages,
  • read help documentation.

What is an R session?

An R session is the current working instance of R.

During a session:

  • objects live in memory,
  • loaded packages are available,
  • results depend on what you have already run.

When you restart R:

  • memory is cleared,
  • packages must be reloaded,
  • scripts must be re-run.

This is why reproducible workflows matter.

Writing and running code

Writing code

Write code in the Source pane, for example:

x <- 5
y <- 10
x + y
[1] 15

R & RStudio Keyboard Shortcuts (Quick Reference)

Using keyboard shortcuts will make you faster, reduce errors, and improve reproducibility. These shortcuts work in RStudio unless otherwise noted.

Running code

Action Windows / Linux macOS
Run current line / selection Ctrl + Enter Cmd + Enter
Run entire script Ctrl + Alt + R Cmd + Option + R
Re-run previous command ↑ ↑

Code editing & formatting

Action Windows / Linux macOS
Comment / uncomment lines Ctrl + Shift + C Cmd + Shift + C
Re-indent code Ctrl + I Cmd + I
Auto-complete Tab Tab
Show function arguments Ctrl + Shift + A Cmd + Shift + A
Rename object (refactor) Ctrl + Shift + Alt + M Cmd + Shift + Option + M

Working with files & panes

Action Windows / Linux macOS
New script Ctrl + Shift + N Cmd + Shift + N
Save file Ctrl + S Cmd + S
Switch between tabs Ctrl + Tab Cmd + Option + →
Zoom active pane Ctrl + Shift + 1 Cmd + Shift + 1
Restore panes Ctrl + Shift + 0 Cmd + Shift + 0

Console & environment management

Action Windows / Linux macOS
Clear console Ctrl + L Cmd + L
Interrupt running code Esc Esc
Restart R session Ctrl + Shift + F10 Cmd + Shift + F10
Clear workspace Ctrl + Shift + F9 Cmd + Shift + F9

Pipes & assignment helpers

Action Windows / Linux macOS
Insert pipe %>% or |> Ctrl + Shift + M Cmd + Shift + M
Insert assignment <- Alt + - Option + -

Quarto / R Markdown shortcuts

Action Windows / Linux macOS
Knit / Render document Ctrl + Shift + K Cmd + Shift + K
Insert code chunk Ctrl + Alt + I Cmd + Option + I
Preview Quarto document Ctrl + Shift + K Cmd + Shift + K

Working with R Libraries

In R, the words package and library are often used interchangeably.

A package is a bundle of:

  • functions (tools you can call),
  • datasets (sometimes),
  • documentation (help files),
  • and other code that expands what base R can do.

A library is the collection of installed packages on your computer, and library() is the function we use to load a package for use in the current session.

Why do we need packages?

Base R can do a lot, but packages make R powerful and efficient. We use packages to:

  • clean and reshape data more easily,
  • make high-quality plots,
  • import/export many file types,
  • run statistical models and machine learning,
  • write reproducible reports (Quarto),
  • and share standardized tools across projects.

In this course, you’ll use packages so that everyone can run the same code and get the same results.

Installing vs loading: the key difference

Install a package once (or whenever you update R / need a newer version).
Load a package every time you start a new R session and want to use it.

Think of it like an app:

  • Installing is downloading the app to your phone.
  • Loading is opening the app to use it.

How to install a package

Use install.packages() with the package name in quotes.

The first package we will install is tidyverse. We will also install quarto.