Computing with Data

elgeish

613.2K views

GitHub

Open Source Your Knowledge, Become a Contributor

Technology knowledge has to be shared and made accessible for free. Join the movement.

Create Content

Previous: Loops Next: Running Compiled Code

Vectorization

It is best to avoid loops when programming in R. There are two reasons for this: simplifying code and computational speed-up. Many mathematical computations on lists, vectors, or arrays may be performed without loops using component-wise arithmetic. The code below demonstrates the computational speedup resulting from replacing a loop with vectorized code:

a = 1:10
c = 0  # compute sum of squares using a for-loop
for (e in a) c = c + e^2
c
sum(a ^ 2) # same operation using vector arithmetic
a = 1:1000000
c = 0
# time comparison with a million elements
system.time(for (e in a) c = c + e ^ 2)
system.time(sum(a ^ 2))
# The sapply function simplifies code but the computational speed-up may not
# apply in the same way as it did above
a = seq(0, 1, length.out = 10)
b = 0
c = 0
for (e in a) {
    b = b + exp(e)
}
b
c = sum(sapply(a, exp))
c
# sapply with an anonymous function f(x) = exp(x ^ 2)
sum(sapply(a, function(x) { return(exp(x ^ 2)) }))
# or simply
sum(sapply(a, function(x) exp(x ^ 2)))

Open Source Your Knowledge: become a Contributor and help others learn. Create New Content

Open Source Your Knowledge, Become a Contributor

183/300 Vectorization

Vectorization

PYTHON: BEGINNER QUIZ (10 Questions)

PYTHON STRING METHODS: center(), rjust() and ljust().

Python from Zero to Hero

Beginner Python Concepts