- Write conditional statements with
`if`

and`else`

. - Write and understand
`while`

and`for`

loops.

Often when we're coding we want to control the flow of our actions. This can be done by setting actions to occur only if a condition or a set of conditions are met. Alternatively, we can also set an action to occur a particular number of times.

There are several ways you can control flow in R. For conditional statements, the most commonly used approaches are the constructs:

```
# if
if (condition is true) {
perform action
}
# if ... else
if (condition is true) {
perform action
}
else { # that is, if the condition is false,
perform alternative action
}
```

Say, for example, that we want R to print a message if a variable `x`

has a particular value:

```
# sample a random number from a Poisson distribution
# with a mean (lambda) of 8
x <- rpois(1, lambda=8)
if (x >= 10) {
print("x is greater than or equal to 10")
}
x
```

Note you may not get the same output as your neighbour because you may be sampling different random numbers from the same distribution.

Let's set a seed so that we all generate the same 'pseudo-random' number, and then print more information:

```
set.seed(10)
x <- rpois(1, lambda=8)
if (x >= 10) {
print("x is greater than or equal to 10")
} else if (x > 5) {
print("x is greater than 5")
} else {
print("x is less than 5")
}
```

`[1] "x is greater than 5"`

In the above case, the function `rpois`

generates a random number following a Poisson distribution with a mean (i.e. lambda) of 8. The function `set.seed`

guarantees that all machines that use the seed 10 as an input, will generate the exact same 'pseudo-random' number (more about pseudo-random numbers). Now, looking at x we see that it takes the value 8 (you should get the exact same number).

**Important:** when R evaluates the condition inside `if`

statements, it is looking for a logical element, i.e., `TRUE`

or `FALSE`

. This can cause some headaches for beginners. For example:

```
x <- 4 == 3
if (x) {
"4 equals 3"
}
```

As we can see, the message was not printed because the vector x is `FALSE`

```
x <- 4 == 3
x
```

`[1] FALSE`

Use an `if`

statement to print a suitable message reporting whether there are any records from 2002 in the `gapminder`

dataset. Now do the same for 2012.

Did anyone get a warning message like this?

```
Warning message:
In if (gapminder$year == 2012) { :
the condition has length > 1 and only the first element will be used
```

If your condition evaluates to a vector with more than one logical element, the function `if`

will still run, but will only evaluate the condition in the first element. Here you need to make sure your condition is of length 1.

## Tip:

`any`

and`all`

The

`any`

function will return TRUE if at least one TRUE value is found within a vector, otherwise it will return`FALSE`

. This can be used in a similar way to the`%in%`

operator. The function`all`

, as the name suggests, will only return`TRUE`

if all values in the vector are`TRUE`

.

Sometimes you will find yourself needing to repeat an operation until a certain condition is met. You can do this with a `while`

loop.

```
while(this condition is true){
do a thing
}
```

Let's try an example, shall we? We'll try to come up with some simple code that generates random numbers from a uniform distribution (the `runif`

function) between 0 and 1 until it gets one that's less than 0.1.

```
while(z > 0.1){
z <- runif(1)
print(z)
}
```

`Error: object 'z' not found`

But wait, that doesn't work! What's the problem?

The problem is that we haven't defined `z`

, and so the very first time the while loop's condition is checked (`z > 0.1`

), `while`

just says "Okay, that's not true so I'm not going to execute this block of code". The same thing would have happened if we defined `z`

to be anything less than 0.1. Let's fix it.

```
z <- 1
while(z > 0.1){
z <- runif(1)
print(z)
}
```

Use a `while`

loop to construct a vector called 'pet_list' with the value: 'cat', 'dog', 'dog', 'dog', 'dog' (N.B. using a loop may not be the most efficient way to do this, but it illustrates the principle!)

`while`

loops will not always be appropriate. If you want to iterate over a set of values, when the order of iteration is important, and perform the same operation on each, a `for`

loop will do the job. We saw `for`

loops in the shell lessons earlier. This is the most flexible of looping operations, but therefore also the hardest to use correctly. Avoid using `for`

loops unless the order of iteration is important: i.e. the calculation at each iteration depends on the results of previous iterations.

The basic structure of a `for`

loop is:

```
for(iterator in set of values){
do a thing
}
```

For example:

```
for(i in 1:10){
print(i)
}
```

The `1:10`

bit creates a vector on the fly; you can iterate over any other vector as well.

We can use a `for`

loop nested within another `for`

loop to iterate over two things at once.

```
for (i in 1:5){
for(j in c('a', 'b', 'c', 'd', 'e')){
print(paste(i,j))
}
}
```

Rather than printing the results, we could write the loop output to a new object.

```
output_vector <- c()
for (i in 1:5){
for(j in c('a', 'b', 'c', 'd', 'e')){
temp_output <- paste(i, j)
output_vector <- c(output_vector, temp_output)
}
}
output_vector
```

```
[1] "1 a" "1 b" "1 c" "1 d" "1 e" "2 a" "2 b" "2 c"
[9] "2 d" "2 e" "3 a" "3 b" "3 c" "3 d" "3 e" "4 a"
[17] "4 b" "4 c" "4 d" "4 e" "5 a" "5 b" "5 c" "5 d"
[25] "5 e"
```

This approach can be useful, but 'growing your results' (building the result object incrementally) is computationally inefficient, so avoid it when you are iterating through a lot of values.

One of the biggest things that trips up novices and experienced R users alike, is building a results object (vector, list, matrix, data frame) as your for loop progresses. Computers are very bad at handling this, so your calculations can very quickly slow to a crawl. It's much better to define an empty results object before hand of the appropriate dimensions. So if you know the end result will be stored in a matrix like above, create an empty matrix with 5 row and 5 columns, then at each iteration store the results in the appropriate location.

A better way is to define your (empty) output object before filling in the values. For this example, it looks more involved, but is still more efficient.

```
output_matrix <- matrix(nrow=5, ncol=5)
j_vector <- c('a', 'b', 'c', 'd', 'e')
for (i in 1:5){
for(j in 1:5){
temp_j_value <- j_vector[j]
temp_output <- paste(i, temp_j_value)
output_matrix[i, j] <- temp_output
}
}
output_vector2 <- as.vector(output_matrix)
output_vector2
```

Compare the objects output_vector and output_vector2. Are they the same? If not, why not? How would you change the last block of code to make output_vector2 the same as output_vector?

Write a script that loops through the `gapminder`

data by continent and prints out whether the mean life expectancy is smaller or larger than 50 years.

Modify the script from Challenge 4 to also loop over each country. This time print out whether the life expectancy is smaller than 50, between 50 and 70, or greater than 70.

Write a script that loops over each country in the `gapminder`

dataset, tests whether the country starts with a 'B', and graphs life expectancy against time as a line graph if the mean life expectancy is under 50 years.