Data Structures in R

R's true power comes from its rich set of built-in data structures. Understanding vectors, matrices, arrays, and factors is essential before moving on to data frames.

1. Vectors — The Building Block of R

A vector is an ordered collection of elements of the same data type. It's the most fundamental structure in R — even a single number is technically a vector of length 1.

numbers <- c(10, 20, 30, 40)
names <- c("Alice", "Bob", "Carol")
flags <- c(TRUE, FALSE, TRUE)

length(numbers)   # 4
numbers[2]        # 20 (1-indexed!)
numbers[c(1, 3)]  # 10 30
numbers[numbers > 15]  # 20 30 40 (logical subsetting)

2. Vector Operations Are Vectorized

Arithmetic on vectors is applied element-wise automatically — no loops needed:

a <- c(1, 2, 3)
b <- c(10, 20, 30)
a + b        # 11 22 33
a * 2        # 2 4 6
sum(a)       # 6
mean(b)      # 20

3. Matrices — Two-Dimensional Data

A matrix is a 2D structure where all elements share the same type.

m <- matrix(1:6, nrow = 2, ncol = 3)
print(m)
#      [,1] [,2] [,3]
# [1,]    1    3    5
# [2,]    2    4    6

m[1, 2]     # 3 (row 1, col 2)
m[, 2]      # entire column 2: 3 4
m[1, ]      # entire row 1: 1 3 5
t(m)        # transpose
dim(m)      # 2 3

4. Arrays — Multi-Dimensional Extensions

An array generalizes a matrix to more than two dimensions:

arr <- array(1:24, dim = c(2, 3, 4))
dim(arr)      # 2 3 4
arr[1, 2, 3]  # single element at that position

5. Factors — Categorical Data

A factor stores categorical variables with fixed, known levels — ideal for things like survey responses, grades, or groups in statistical models.

grades <- factor(c("A", "B", "A", "C", "B"),
                  levels = c("A", "B", "C"))
levels(grades)   # "A" "B" "C"
table(grades)    # frequency count per level

# Ordered factor (has a meaningful order)
size <- factor(c("Small", "Large", "Medium"),
               levels = c("Small", "Medium", "Large"),
               ordered = TRUE)
size[1] < size[2]  # TRUE (Small < Large)

6. Comparing the Four Structures

7. Production-Ready Checklist

• ✅ Master vector creation and indexing — the foundation of everything else in R.
• ✅ Understand vectorized operations — avoid unnecessary loops.
• ✅ Use matrices for 2D single-type data — and arrays for higher dimensions.
• ✅ Use factors for categorical variables — especially before statistical modeling.