RStudio Setup, R Basics & Hello World

RStudio Setup, R Basics & Hello World

Why R?

R is the language of choice for statisticians, data scientists, and researchers worldwide. Unlike Python's general-purpose approach, R was built specifically for data analysis — statistics, visualization, and reproducible research are first-class citizens.

Key strengths:

• Best-in-class visualization: ggplot2 produces publication-quality graphics
• Statistical depth: thousands of statistical tests built in, with packages for every specialized method
• Data frames: tabular data handling is native, not an afterthought
• Reproducibility: R Markdown and Quarto combine code + narrative + output in one document

R is used at Google, Facebook, Airbnb, the FDA, and in every major academic journal for data analysis.

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Installing R and RStudio

Step 1: Install R (the language)

1. Go to https://cran.r-project.org
2. Click your OS: Windows, macOS, or Linux
3. Download and run the installer
4. Verify: open a terminal and run R --version

Step 2: Install RStudio (the IDE)

1. Go to https://posit.co/download/rstudio-desktop/
2. Download RStudio Desktop (Free)
3. Install and open it

The RStudio Layout:

• Console (bottom-left): interactive R session — type code, see results
• Script Editor (top-left): write and save .R files
• Environment (top-right): all variables in memory
• Plots/Files/Packages (bottom-right): view charts, install packages

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Running Code Two Ways

Interactive console — type directly, press Enter:

> 2 + 2
[1] 4
> "Hello, World!"
[1] "Hello, World!"

The [1] means the first element of the result vector.

Script file — write a .R file, click Run or Source:

# This is a comment in R
x <- 10        # assign 10 to x
y <- 3         # assign 3 to y
z <- x + y     # z = 13

cat("Sum:", z, "\n")  # cat() for formatted output (no newline by default)
print(z)              # print() outputs with [1]
message("Done!")      # message() writes to stderr

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Basic Data Types

# Numeric (double by default)
x <- 42.5
class(x)     # "numeric"
typeof(x)    # "double"

# Integer (append L)
n <- 10L
class(n)     # "integer"

# Character (strings)
name <- "Jordan"
class(name)  # "character"

# Logical
is_active <- TRUE
class(is_active)  # "logical"
# TRUE/FALSE (uppercase) or T/F (shorthand)

# Complex
z <- 3 + 2i
class(z)  # "complex"

# NULL — absence of value
nothing <- NULL
is.null(nothing)  # TRUE

# NA — missing value (different from NULL!)
missing_val <- NA
is.na(missing_val)  # TRUE

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Arithmetic and String Operations

# Arithmetic
10 + 3   # 13
10 - 3   # 7
10 * 3   # 30
10 / 3   # 3.333...
10 %% 3  # 1   (modulo)
10 %/% 3 # 3   (integer division)
2 ^ 10   # 1024 (exponentiation — use ^ not **)
sqrt(144) # 12
abs(-5)   # 5

# String operations
name <- "Jordan Morris"
nchar(name)              # 13
toupper(name)            # "JORDAN MORRIS"
tolower(name)            # "jordan morris"
substr(name, 1, 6)       # "Jordan" (1-indexed!)
gsub("Jordan", "Alice", name)  # "Alice Morris"
paste("Hello", "World")        # "Hello World" (space sep)
paste0("Hello", "World")       # "HelloWorld" (no sep)
sprintf("Score: %.1f%%", 87.5) # "Score: 87.5%"

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Your First Complete R Script

# Temperature Analysis Script
# Run this with: source("temperature.R")

temps_c <- c(22, 18, 25, 30, 15, 28, 21)  # Celsius readings
n <- length(temps_c)

# Vectorized conversion — applies to ALL elements at once
temps_f <- temps_c * 9/5 + 32

# Built-in statistics
cat("=== Temperature Analysis ===\n")
cat(sprintf("Readings: %d\n", n))
cat(sprintf("Celsius:     Mean=%.1f, SD=%.1f, Range=[%.1f, %.1f]\n",
    mean(temps_c), sd(temps_c), min(temps_c), max(temps_c)))
cat(sprintf("Fahrenheit:  Mean=%.1f, SD=%.1f, Range=[%.1f, %.1f]\n",
    mean(temps_f), sd(temps_f), min(temps_f), max(temps_f)))

# Which days were hot? (vectorized logical comparison)
hot_days <- which(temps_c > 25)  # indices where temp > 25
cat(sprintf("Hot days (>25C): %s\n", paste(hot_days, collapse=", ")))

# Summary
summary(temps_c)

Key insight: temps_c * 9/5 + 32 converts ALL 7 temperatures at once — no loop needed. This is R's vectorization in action.