Vectors, lists, and matrices — R Module 3 | LeadAfrik Public Economics Hub

Vectors, lists, and matrices are the three building blocks of R data structures. Get the subsetting rules right and the rest of the language follows.

Vectors — homogeneous, 1D

v <- c(10, 20, 30, 40, 50)
v[1]            # 10 (R is 1-indexed!)
v[c(1, 3, 5)]   # 10 30 50 — multiple positions
v[-1]           # 20 30 40 50 — exclude position 1
v[v > 20]       # 30 40 50 — boolean mask

# Named vector
named <- c(KCB = 1500, Equity = 1700, Coop = 600)
named["Equity"] # 1700

R is 1-indexed

v[1] is the first element, not the second. Coming from Python or any C-family language, this catches you for a week then fades. R is closer to math notation here than to programming convention.

Lists — heterogeneous, 1D

Lists hold arbitrary objects of different types. Use them when you need to bundle a mix of values, vectors, and other lists.

result <- list(
    name = "KCB",
    assets = 1500,
    rates = c(0.10, 0.12, 0.14)
)

result$name        # "KCB" — using $ for named access
result[["assets"]] # 1500 — using double brackets
result[[3]]        # the rates vector

[ vs [[

Single brackets [ return a list (or vector) of the selected elements. Double brackets [[ extract a single element. The classic example: result[1] is a list of length 1 containing the name; result[[1]] is the name itself.

Matrices — homogeneous, 2D

m <- matrix(1:12, nrow = 3, ncol = 4)
m[1, ]    # row 1 — a vector
m[, 1]    # column 1
m[1, 2]   # element at row 1, col 2

# Matrix arithmetic
m * 2     # element-wise
m %*% t(m) # matrix multiplication (not *)
solve(m %*% t(m)) # matrix inverse

Exercise

Create a vector of c(0.07, 0.10, 0.12, 0.15, 0.08) and select all rates above 0.10.