# 13 Control Structures

Watch a video of this section

Control structures in R allow you to control the flow of execution of a series of R expressions. Basically, control structures allow you to put some “logic” into your R code, rather than just always executing the same R code every time. Control structures allow you to respond to inputs or to features of the data and execute different R expressions accordingly.

Commonly used control structures are

• if and else: testing a condition and acting on it

• for: execute a loop a fixed number of times

• while: execute a loop while a condition is true

• repeat: execute an infinite loop (must break out of it to stop)

• break: break the execution of a loop

• next: skip an interation of a loop

Most control structures are not used in interactive sessions, but rather when writing functions or longer expresisons. However, these constructs do not have to be used in functions and it’s a good idea to become familiar with them before we delve into functions.

## 13.1if-else

Watch a video of this section

The if-else combination is probably the most commonly used control structure in R (or perhaps any language). This structure allows you to test a condition and act on it depending on whether it’s true or false.

For starters, you can just use the if statement.

if(<condition>) {
## do something
}
## Continue with rest of code

The above code does nothing if the condition is false. If you have an action you want to execute when the condition is false, then you need an else clause.

if(<condition>) {
## do something
}
else {
## do something else
}

You can have a series of tests by following the initial if with any number of else ifs.

if(<condition1>) {
## do something
} else if(<condition2>)  {
## do something different
} else {
## do something different
}

Here is an example of a valid if/else structure.

## Generate a uniform random number
x <- runif(1, 0, 10)
if(x > 3) {
y <- 10
} else {
y <- 0
}

The value of y is set depending on whether x > 3 or not. This expression can also be written a different, but equivalent, way in R.

y <- if(x > 3) {
10
} else {
0
}

Neither way of writing this expression is more correct than the other. Which one you use will depend on your preference and perhaps those of the team you may be working with.

Of course, the else clause is not necessary. You could have a series of if clauses that always get executed if their respective conditions are true.

if(<condition1>) {

}

if(<condition2>) {

}

## 13.2for Loops

Watch a video of this section

For loops are pretty much the only looping construct that you will need in R. While you may occasionally find a need for other types of loops, in my experience doing data analysis, I’ve found very few situations where a for loop wasn’t sufficient.

In R, for loops take an interator variable and assign it successive values from a sequence or vector. For loops are most commonly used for iterating over the elements of an object (list, vector, etc.)

> for(i in 1:10) {
+         print(i)
+ }
[1] 1
[1] 2
[1] 3
[1] 4
[1] 5
[1] 6
[1] 7
[1] 8
[1] 9
[1] 10

This loop takes the i variable and in each iteration of the loop gives it values 1, 2, 3, …, 10, executes the code within the curly braces, and then the loop exits.

The following three loops all have the same behavior.

> x <- c("a", "b", "c", "d")
>
> for(i in 1:4) {
+         ## Print out each element of 'x'
+         print(x[i])
+ }
[1] "a"
[1] "b"
[1] "c"
[1] "d"

The seq_along() function is commonly used in conjunction with for loops in order to generate an integer sequence based on the length of an object (in this case, the object x).

> ## Generate a sequence based on length of 'x'
> for(i in seq_along(x)) {
+         print(x[i])
+ }
[1] "a"
[1] "b"
[1] "c"
[1] "d"

It is not necessary to use an index-type variable.

> for(letter in x) {
+         print(letter)
+ }
[1] "a"
[1] "b"
[1] "c"
[1] "d"

For one line loops, the curly braces are not strictly necessary.

> for(i in 1:4) print(x[i])
[1] "a"
[1] "b"
[1] "c"
[1] "d"

However, I like to use curly braces even for one-line loops, because that way if you decide to expand the loop to multiple lines, you won’t be burned because you forgot to add curly braces (and you will be burned by this).

## 13.3 Nested for loops

for loops can be nested inside of each other.

x <- matrix(1:6, 2, 3)

for(i in seq_len(nrow(x))) {
for(j in seq_len(ncol(x))) {
print(x[i, j])
}
}

Nested loops are commonly needed for multidimensional or hierarchical data structures (e.g. matrices, lists). Be careful with nesting though. Nesting beyond 2 to 3 levels often makes it difficult to read/understand the code. If you find yourself in need of a large number of nested loops, you may want to break up the loops by using functions (discussed later).

## 13.4while Loops

Watch a video of this section

While loops begin by testing a condition. If it is true, then they execute the loop body. Once the loop body is executed, the condition is tested again, and so forth, until the condition is false, after which the loop exits.

> count <- 0
> while(count < 10) {
+         print(count)
+         count <- count + 1
+ }
[1] 0
[1] 1
[1] 2
[1] 3
[1] 4
[1] 5
[1] 6
[1] 7
[1] 8
[1] 9

While loops can potentially result in infinite loops if not written properly. Use with care!

Sometimes there will be more than one condition in the test.

> z <- 5
> set.seed(1)
>
> while(z >= 3 && z <= 10) {
+         coin <- rbinom(1, 1, 0.5)
+
+         if(coin == 1) {  ## random walk
+                 z <- z + 1
+         } else {
+                 z <- z - 1
+         }
+ }
> print(z)
[1] 2

Conditions are always evaluated from left to right. For example, in the above code, if z were less than 3, the second test would not have been evaluated.

## 13.5repeat Loops

Watch a video of this section

repeat initiates an infinite loop right from the start. These are not commonly used in statistical or data analysis applications but they do have their uses. The only way to exit a repeat loop is to call break.

One possible paradigm might be in an iterative algorith where you may be searching for a solution and you don’t want to stop until you’re close enough to the solution. In this kind of situation, you often don’t know in advance how many iterations it’s going to take to get “close enough” to the solution.

x0 <- 1
tol <- 1e-8

repeat {
x1 <- computeEstimate()

if(abs(x1 - x0) < tol) {  ## Close enough?
break
} else {
x0 <- x1
}
}

Note that the above code will not run if the computeEstimate() function is not defined (I just made it up for the purposes of this demonstration).

The loop above is a bit dangerous because there’s no guarantee it will stop. You could get in a situation where the values of x0 and x1 oscillate back and forth and never converge. Better to set a hard limit on the number of iterations by using a for loop and then report whether convergence was achieved or not.

## 13.6next, break

next is used to skip an iteration of a loop.

for(i in 1:100) {
if(i <= 20) {
## Skip the first 20 iterations
next
}
## Do something here
}

break is used to exit a loop immediately, regardless of what iteration the loop may be on.

for(i in 1:100) {
print(i)

if(i > 20) {
## Stop loop after 20 iterations
break
}
}

## 13.7 Summary

• Control structures like if, while, and for allow you to control the flow of an R program

• Infinite loops should generally be avoided, even if (you believe) they are theoretically correct.

• Control structures mentioned here are primarily useful for writing programs; for command-line interactive work, the “apply” functions are more useful.