Data Structures

# Numeric vector
nums <- c(10, 20, 30, 40, 50)

# Character vector
colors <- c("red", "green", "blue")

# Logical vector
flags <- c(TRUE, FALSE, TRUE, TRUE)

# Named vector
ages <- c(Alice = 25, Bob = 30, Charlie = 35)
ages["Bob"]  # 30

# Vector operations
length(nums)    # 5
sort(nums, decreasing = TRUE)
rev(nums)       # Reverse
unique(c(1,1,2,3))  # 1 2 3

# Create a 3x4 matrix
mat <- matrix(1:12, nrow = 3, ncol = 4)
#      [,1] [,2] [,3] [,4]
# [1,]    1    4    7   10
# [2,]    2    5    8   11
# [3,]    3    6    9   12

# Fill by row instead of column
mat2 <- matrix(1:12, nrow = 3, byrow = TRUE)

# Access elements
mat[1, 2]     # Row 1, Col 2 = 4
mat[1, ]      # Entire row 1
mat[, 2]      # Entire column 2

# Matrix operations
dim(mat)       # 3 4
nrow(mat)      # 3
ncol(mat)      # 4
t(mat)         # Transpose
mat * 2        # Element-wise multiplication
mat %*% t(mat) # Matrix multiplication

# 3D array: 2 rows, 3 cols, 2 "layers"
arr <- array(1:12, dim = c(2, 3, 2))
arr[1, 2, 1]  # Row 1, Col 2, Layer 1

# Named list
person <- list(
  name = "Alice",
  age = 30,
  scores = c(95, 87, 92),
  active = TRUE
)

# Access elements
person$name         # "Alice"
person[["age"]]     # 30
person[[3]]         # c(95, 87, 92)

# Nested list
company <- list(
  name = "Acme Corp",
  employees = list(
    list(name = "Alice", role = "Engineer"),
    list(name = "Bob", role = "Designer")
  )
)
company$employees[[1]]$name  # "Alice"

# Modify a list
person$email <- "alice@example.com"  # Add element
person$age <- 31                    # Update element

# Create a data frame
df <- data.frame(
  name = c("Alice", "Bob", "Charlie"),
  age = c(25, 30, 35),
  salary = c(50000, 60000, 70000),
  stringsAsFactors = FALSE
)

# Access columns
df$name          # "Alice" "Bob" "Charlie"
df[, "age"]      # 25 30 35
df[1, ]          # First row

# Filter rows
df[df$age > 28, ]   # Rows where age > 28

# Add a column
df$bonus <- df$salary * 0.1

# Summary
str(df)       # Structure
summary(df)   # Statistical summary
nrow(df)      # 3
ncol(df)      # 4
head(df, 2)  # First 2 rows

# Create a factor
sizes <- factor(c("S", "M", "L", "M", "S", "XL"))
levels(sizes)  # "L"  "M"  "S"  "XL" (alphabetical by default)

# Ordered factor
sizes_ord <- factor(
  c("S", "M", "L", "M", "S"),
  levels = c("S", "M", "L", "XL"),
  ordered = TRUE
)
sizes_ord[1] < sizes_ord[2]  # TRUE (S < M)

# Table of counts
table(sizes)
#  L  M  S XL
#  1  2  2  1