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---
title : Introduction to the R Language
subtitle : Control Structures
author : Roger Peng, Associate Professor
job : Johns Hopkins Bloomberg School of Public Health
logo : bloomberg_shield.png
framework : io2012 # {io2012, html5slides, shower, dzslides, ...}
highlighter : highlight.js # {highlight.js, prettify, highlight}
hitheme : tomorrow #
url:
lib: ../../libraries
assets: ../../assets
widgets : [mathjax] # {mathjax, quiz, bootstrap}
mode : selfcontained # {standalone, draft}
---
## Control Structures
Control structures in R allow you to control the flow of execution of the program, depending on runtime conditions. Common structures are
- `if`, `else`: testing a condition
- `for`: execute a loop a fixed number of times
- `while`: execute a loop _while_ a condition is true
- `repeat`: execute an infinite loop
- `break`: break the execution of a loop
- `next`: skip an interation of a loop
- `return`: exit a function
Most control structures are not used in interactive sessions, but rather when writing functions or longer expresisons.
---
## Control Structures: if
```r
if(<condition>) {
## do something
} else {
## do something else
}
if(<condition1>) {
## do something
} else if(<condition2>) {
## do something different
} else {
## do something different
}
```
---
## if
This is a valid if/else structure.
```r
if(x > 3) {
y <- 10
} else {
y <- 0
}
```
So is this one.
```r
y <- if(x > 3) {
10
} else {
0
}
```
---
## if
Of course, the else clause is not necessary.
```r
if(<condition1>) {
}
if(<condition2>) {
}
```
---
## for
`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.)
```r
for(i in 1:10) {
print(i)
}
```
This loop takes the `i` variable and in each iteration of the loop gives it values 1, 2, 3, ..., 10, and then exits.
---
## for
These three loops have the same behavior.
```r
x <- c("a", "b", "c", "d")
for(i in 1:4) {
print(x[i])
}
for(i in seq_along(x)) {
print(x[i])
}
for(letter in x) {
print(letter)
}
for(i in 1:4) print(x[i])
```
---
## Nested for loops
`for` loops can be nested.
```r
x <- matrix(1:6, 2, 3)
for(i in seq_len(nrow(x))) {
for(j in seq_len(ncol(x))) {
print(x[i, j])
}
}
```
Be careful with nesting though. Nesting beyond 2–3 levels is often very difficult to read/understand.
---
## while
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.
```r
count <- 0
while(count < 10) {
print(count)
count <- count + 1
}
```
While loops can potentially result in infinite loops if not written properly. Use with care!
---
## while
Sometimes there will be more than one condition in the test.
```r
z <- 5
while(z >= 3 && z <= 10) {
print(z)
coin <- rbinom(1, 1, 0.5)
if(coin == 1) { ## random walk
z <- z + 1
} else {
z <- z - 1
}
}
```
Conditions are always evaluated from left to right.
---
## repeat
Repeat initiates an infinite loop; these are not commonly used in statistical applications but they do have their uses. The only way to exit a `repeat` loop is to call `break`.
```r
x0 <- 1
tol <- 1e-8
repeat {
x1 <- computeEstimate()
if(abs(x1 - x0) < tol) {
break
} else {
x0 <- x1
}
}
```
---
## repeat
The loop in the previous slide is a bit dangerous because there’s no guarantee it will stop. Better to set a hard limit on the number of iterations (e.g. using a for loop) and then report whether convergence was achieved or not.
---
## next, return
`next` is used to skip an iteration of a loop
```r
for(i in 1:100) {
if(i <= 20) {
## Skip the first 20 iterations
next
}
## Do something here
}
```
`return` signals that a function should exit and return a given value
---
## Control Structures
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 they are theoretically correct.
- Control structures mentiond here are primarily useful for writing programs; for command-line interactive work, the *apply functions are more useful.