R Getting Started — Installation, Setup, and First Steps

Learning Objectives

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

• Install R and RStudio on Windows, macOS, or Linux
• Navigate the RStudio IDE and understand its four panes
• Execute R commands in the console and script editor
• Understand R's basic syntax, help system, and working directory
• Write and run your first R scripts

What Is R?

R is a programming language and environment designed for statistical computing, data analysis, and graphics. Created in 1993 by Ross Ihaka and Robert Gentleman at the University of Auckland, R has become the lingua franca of data science, bioinformatics, and academic research.

R vs Python for Data Science

Installing R

Step 1: Download R

1. Go to https://cran.r-project.org/
2. Click "Download R for [your OS]"
3. Choose the latest stable version (e.g., R 4.4.x)

Step 2: Install R

Windows:

• Run the downloaded .exe file
• Accept default settings
• Choose 64-bit version if prompted

macOS:

• Open the .pkg file
• Follow the installer prompts

Linux (Ubuntu/Debian):

sudo apt update
sudo apt install r-base

Step 3: Verify Installation

Open a terminal or command prompt:

R --version

Installing RStudio

RStudio is the most popular IDE (Integrated Development Environment) for R.

1. Go to https://posit.co/download/rstudio-desktop/
2. Download the free version for your OS
3. Install and launch RStudio

RStudio Interface

RStudio has four panes:

Keyboard Shortcuts:

• Ctrl + Enter — Run current line from editor to console
• Ctrl + Shift + C — Comment/uncomment lines
• Ctrl + Shift + M — Insert pipe operator (|> or %>%)
• Tab — Auto-complete

The R Console

The R console is where you execute commands interactively. Type a command and press Enter.

# Basic arithmetic
2 + 3
# [1] 5

100 / 3
# [1] 33.33333

# Assigning values
x <- 42
x
# [1] 42

# You can also use = for assignment (but <- is conventional)
y = 100
y
# [1] 100

# Printing
print("Hello, R!")
# [1] "Hello, R!"

cat("Hello,", "R!", "\n")
# Hello, R!

R Syntax Basics

Assignment Operators

R supports three assignment operators, but <- is the convention:

# Preferred: arrow operator
x <- 10

# Also valid (but less common in R)
x = 10

# Assignment to the right (rare, used in some packages)
10 -> x

# Multiple assignment
a <- b <- c <- 0
a; b; c
# [1] 0
# [1] 0
# [1] 0

Comments

# This is a comment
# R ignores comments

x <- 5  # Inline comment

# Multi-line
# Line 1
# Line 2

Semicolons

R does not require semicolons, but they work:

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

Working Directory

Your working directory is where R reads and saves files by default.

# Check current working directory
getwd()
# [1] "C:/Users/YourName/Documents"

# Set working directory
setwd("C:/my_project/data")

# Use forward slashes or double backslashes
setwd("C:\\my_project\\data")

# List files in current directory
list.files()

Tip in RStudio: Session → Set Working Directory → Choose Directory

The R Help System

R has extensive built-in help. This is one of R's greatest strengths.

# Get help on a function
?mean
help(mean)

# Get help on a package
help(package = "ggplot2")

# Search help pages
??linear regression
help.search("regression")

# Examples from help
example("mean")

# Vignettes (long-form tutorials)
vignette("dplyr")

Reading Help Pages

Help pages follow a standard structure:

# Help for the mean function
?mean

# Arguments: x, trim, na.rm
# Usage: mean(x, trim = 0, na.rm = FALSE, ...)

# Try the examples
mean(c(1, 2, 3, 4, 5))
# [1] 3

mean(c(1, 2, NA, 4, 5), na.rm = TRUE)
# [1] 3

Data Types Overview

R has six basic data types:

# Numeric (double by default)
x <- 3.14
class(x)
# [1] "numeric"

# Integer (use L suffix)
y <- 42L
class(y)
# [1] "integer"

# Character
name <- "Alice"
class(name)
# [1] "character"

# Logical
is_active <- TRUE
class(is_active)
# [1] "logical"

# Complex
z <- 3 + 4i
class(z)
# [1] "complex"

Data Structures Overview

R has five main data structures:

# Vector
v <- c(1, 2, 3, 4, 5)

# Matrix
m <- matrix(1:9, nrow = 3, ncol = 3)

# List
l <- list(1, "hello", TRUE, 3.14)

# Data Frame
df <- data.frame(
  name = c("Alice", "Bob"),
  age = c(25, 30)
)

Your First R Script

Create a new file in RStudio: File → New File → R Script

# ============================================
# My First R Script
# ============================================

# 1. Basic calculations
result <- 2 + 3
cat("2 + 3 =", result, "\n")

# 2. Working with vectors
scores <- c(85, 92, 78, 95, 88)
cat("Scores:", scores, "\n")
cat("Mean:", mean(scores), "\n")
cat("Max:", max(scores), "\n")
cat("Min:", min(scores), "\n")

# 3. A simple function
bmi <- function(weight_kg, height_m) {
  weight_kg / (height_m ^ 2)
}

my_bmi <- bmi(70, 1.75)
cat("BMI:", round(my_bmi, 1), "\n")

# 4. Conditional
if (my_bmi < 18.5) {
  cat("Category: Underweight\n")
} else if (my_bmi < 25) {
  cat("Category: Normal weight\n")
} else if (my_bmi < 30) {
  cat("Category: Overweight\n")
} else {
  cat("Category: Obese\n")
}

# 5. Simple plot
x <- 1:100
y <- sin(x / 10)
plot(x, y, type = "l", col = "blue",
     main = "Sine Wave",
     xlab = "X", ylab = "sin(x/10)")

Save as my_first_script.R and run with Ctrl + Shift + Enter.

R Packages

Packages extend R's functionality. The Comprehensive R Archive Network (CRAN) hosts 20,000+ packages.

# Install a package (only once per R version)
install.packages("ggplot2")

# Load a package (every session)
library(ggplot2)

# Or use the namespace prefix
ggplot2::ggplot()

# See installed packages
installed.packages()

# Update all packages
update.packages()

Essential Packages for Beginners

# Install the tidyverse (recommended for beginners)
install.packages("tidyverse")

# Load it
library(tidyverse)

R Console Tips and Tricks

Command History

# Use Up/Down arrows to cycle through previous commands
# Or use history()
history()

# Search history with Ctrl+R (reverse search)

Clearing the Console

# In RStudio: Ctrl+L

# In R console:
cat("\014")

Exiting R

# Save workspace and quit
q()

# Or in RStudio: Ctrl+Q

# Save workspace image?
# Save to .RData file (not recommended for beginners)

Common Beginner Mistakes

1. Case Sensitivity

# R is case-sensitive
x <- 10
X <- 20
cat(x, X)  # 10 20 — different variables

2. Using Undefined Variables

# This will error
# y + 1
# Error: object 'y' not found

y <- 5
y + 1
# [1] 6

3. Forgetting Quotes for Strings

# Wrong
# name <- Alice  # Error: object 'Alice' not found

# Correct
name <- "Alice"

4. Mixing Assignment Styles

# Convention: use <-
x <- 5

# = works but is discouraged
x = 5

# -> works but is rarely used
5 -> x

Practice Exercises

Exercise 1: Calculator

Write R code to calculate:

1. The area of a circle with radius 5 (area = pi * r^2)
2. The average of 10, 20, 30, 40, 50
3. The remainder when 17 is divided by 5

Solution

# Area of circle
radius <- 5
area <- pi * radius^2
cat("Area:", area, "\n")

# Average
numbers <- c(10, 20, 30, 40, 50)
avg <- mean(numbers)
cat("Average:", avg, "\n")

# Remainder
remainder <- 17 %% 5
cat("Remainder:", remainder, "\n")

Exercise 2: BMI Calculator

Create a function that takes weight (kg) and height (cm), converts height to meters, and returns the BMI. Test it with your own values.

Solution

calculate_bmi <- function(weight_kg, height_cm) {
  height_m <- height_cm / 100
  bmi <- weight_kg / (height_m ^ 2)
  return(round(bmi, 1))
}

my_bmi <- calculate_bmi(70, 175)
cat("My BMI:", my_bmi, "\n")

Key Takeaways

• R is a language for statistics — purpose-built for data analysis and visualization
• Install R first, then RStudio — R is the engine, RStudio is the dashboard
• Use <- for assignment — it's the R convention
• R is case-sensitive — x and X are different variables
• The ? operator is your friend — ?mean gives instant help
• Start with tidyverse — it provides a consistent, modern workflow
• Save scripts, not just console output — reproducibility matters

Next: Learn about R Variables and Data Types — the building blocks of every R program.