Reference

Introduction to R and RStudio

What is R?

R is a powerful programming language and environment specifically designed for statistical computing and data analysis. Originally created by statisticians for statisticians, R has grown into one of the most popular tools for data science, used across academia, healthcare, finance, and industry.

Why Use R?

• Free and Open Source: R is completely free to download and use
• Comprehensive Statistical Tools: Built-in functions for virtually any statistical analysis
• Excellent for Data Visualization: Create publication-quality graphs and plots
• Reproducible Research: Your analysis is documented in code, making it easy to share and repeat
• Active Community: Thousands of packages extending R’s capabilities
• Healthcare & Research Focus: Widely used in clinical research, epidemiology, and bioinformatics

What is RStudio?

RStudio is an Integrated Development Environment (IDE) for R. Think of it this way: - R is the engine (the programming language that does the work) - RStudio is the dashboard (the interface that makes R easier to use)

While you can use R on its own, RStudio provides a much more user-friendly and productive environment.

The RStudio Interface

When you open RStudio, you’ll see four main panes:

Source/Editor Pane (Top Left) - Where you write and save your R scripts - Allows you to edit and run multiple lines of code - Your analysis workflow lives here

Console Pane (Bottom Left) - Where R actually executes commands - You can type commands directly here for quick tests - Shows output and error messages

Environment/History Pane (Top Right) - Environment: Shows all objects, datasets, and variables currently loaded - History: Keeps track of commands you’ve run

Files/Plots/Packages/Help Pane (Bottom Right) - Files: Browse your project files - Plots: View graphs and visualizations - Packages: Manage installed R packages - Help: Access documentation

Your First Steps in R

Basic R as a Calculator

R

# Addition
5 + 3

# Multiplication
10 * 6

# Division
100 / 4

Creating Variables

R

# Store a value
patientage <- 45

# Store text
patientname <- "John Doe"

# Store multiple values
bloodpressure <- c(120, 135, 118, 142, 130)

Getting Help

R

# Get help on a function
?mean

# Search for help
??regression

Key Concepts to Remember

Packages

R’s functionality is extended through packages - collections of functions, data, and documentation. Think of them as apps you install:

R

# Install a package (do this once)
install.packages("ggplot2")

# Load a package (do this each session)
library(ggplot2)

Scripts vs. Console

• Console: For quick tests and one-off commands
• Scripts: For saving your analysis workflow that you can run again later

Comments

Use # to add comments to your code:

R

# This is a comment - R will ignore it
patientcount <- 150  # You can also comment after code

Other tips

• Use the escape key to cancel incomplete commands or running code (Ctrl+C) if you’re using R from the shell.
• Basic arithmetic operations follow standard order of precedence:
  • Brackets: (, )
  • Exponents: ^ or **
  • Divide: /
  • Multiply: *
  • Add: +
  • Subtract: -
• Scientific notation is available, e.g: 2e-3
• Anything to the right of a # is a comment, R will ignore this!
• Functions are denoted by function_name(). Expressions inside the brackets are evaluated before being passed to the function, and functions can be nested.
• Mathematical functions: exp, sin, log, log10, log2 etc.
• Comparison operators: <, <=, >, >=, ==, !=
• Use all.equal to compare numbers!
• <- is the assignment operator. Anything to the right is evaluate, then stored in a variable named to the left.
• ls lists all variables and functions you’ve created
• rm can be used to remove them
• When assigning values to function arguments, you must use =.

Tips for Getting Started

• Use Projects: RStudio Projects keep your work organized
• Save Your Scripts: Your code is your documentation
• Don’t Fear Errors: Error messages help you learn - read them carefully
• Use Tab Completion: Start typing and press Tab for suggestions
• Check Your Working Directory: Know where R is looking for files with getwd(), or use the here library.

Common Beginner Mistakes

• Case Sensitivity: R distinguishes between Data and data
• Missing Commas: Function arguments need commas: mean(x, na.rm = TRUE)
• Unmatched Parentheses: Every ( needs a closing )
• Wrong Assignment Operator: Use <- for assignment, not = (though = works in some contexts)

Next Steps

Once you’re comfortable with the basics:

• Learn to import and explore data
• Master data manipulation with packages like dplyr
• Create visualizations with ggplot2
• Perform statistical analyses relevant to your field
• Generate reports with R Markdown

Resources

• Built-in Help: Type ?function_name` in the console
• Cheat Sheets: RStudio provides excellent cheat sheets (Help → Cheat Sheets)
• Stack Overflow: Great for troubleshooting specific problems
• R for Data Science: Free online book at r4ds.had.co.nz

Remember: Everyone starts as a beginner. The best way to learn R is by doing - start with simple tasks and gradually build your skills. The R community is welcoming and helpful, so don’t hesitate to ask questions!

Project management with RStudio

• To create a new project, go to File -> New Project
• Install the packrat package to create self-contained projects
• install.packages to install packages from CRAN
• library to load a package into R
• packrat::status to check whether all packages referenced in your scripts have been installed.