Mastering R: 100 Ways to Leverage Vectorized Operations in R

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Vectorized operations refer to the execution of operations on entire arrays (vectors, matrices, or higher-dimensional data structures) in a single, efficient step, without the need for explicit loops. This is a key concept in programming languages like R, Python (with libraries like NumPy), and others that are designed for numerical and scientific computing.

In the context of R, vectorized operations take advantage of the underlying C and Fortran implementations to efficiently perform computations on entire vectors or matrices. This is in contrast to using explicit loops, where operations are performed element-wise, often resulting in slower and less concise code.

• Example 1: Element-wise Addition of Two Vectors

# Vectorized
v1 <- c(1, 2, 3)
v2 <- c(4, 5, 6)
result_vectorized <- v1 + v2

# Non-Vectorized (Loop)
v1 <- c(1, 2, 3)
v2 <- c(4, 5, 6)
result_non_vectorized <- numeric(length(v1))

for (i in seq_along(v1)) {
  result_non_vectorized[i] <- v1[i] + v2[i]
}

• Example 2: Broadcasting in Vectorized Operations

# Vectorized Broadcasting
matrix <- matrix(1:6, nrow = 2)
result_broadcasting <- matrix + c(10, 20)

# Non-Vectorized (Loop) Broadcasting
matrix <- matrix(1:6, nrow = 2)
result_non_vectorized_broadcasting <- matrix

for (i in…