This is a short tutorial to explain ‘for loops’.
Comments are noted with #
Results are noted with [1]
rep()
# Often we want to start with a vector of 0's and then modify the entries in later code. R makes this easy with the replicate function rep()
# rep(0, 10) makes a vector of of 10 zeros.
x = rep(0,10)
x
[1] 0 0 0 0 0 0 0 0 0 0
# rep() will replicate almost anything
x = rep(2,6)
x
[1] 2 2 2 2 2 2
x = rep('abc',5)
x
[1] "abc" "abc" "abc" "abc" "abc"
x = rep(1:4,5)
x
[1] 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
for(i in x)
# 'for loops' let us repeat (loop) through the elements in a vector and run the same code on each element
# We will illustrate with examples
# Loop through the sequence 1 to 5 printing the square of each number
for (j in 1:5)
{
print(j^2)
}
class='r'>[1] 1
[1] 4
[1] 9
[1] 16
[1] 25
# We can capture the results of our loop in a list
# First we create a vector and then we fill in its values
n = 5
x = rep(0,n)
for (j in 1:n)
{
x[j] = j^2
}
x
class='r'>[1] 1 4 9 16 25
# You always wanted to know the sum of the first 100 squares.
n = 100
x = rep(0,n)
for (j in 1:n)
{
x[j] = j^2
}
sum(x)
class='r'>[1] 338350
# Let's use a for loop to estimate the average of squaring the result of a roll of a die.
nsides = 6
ntrials = 1000
trials = rep(0,ntrials)
for (j in 1:ntrials)
{
trials[j] = sample(1:nsides,1) # We get one sample at a time
}
mean(trials^2)
[1] 15.207
# Of course we could have done this simulation without a loop.
# for loops are truly valuable when the calculation is more complicated and we can't do it exactly or with built in R functions.
# Let's estimate the probability of a derangement in a permutation of 9 objects. (A derangement is a permutation where no element ends up in its original position.)
n = 9
x = 1:n
ntrials = 10000
trials = rep(0,ntrials)
for (j in 1:ntrials)
{
y = sample(x,n)
s = sum(y == x) # s = number of people in their original seat
trials[j] = (s == 0) # 1 if a derangement, 0 if not
}
mean(trials) # mean(trials) = fraction that are 1's
[1] 0.3697
