# Intermediate R for reproducible scientific analysis

## Apply functions

## Learning objectives

- To learn how to use
*apply*to automate tasks efficiently - To know the difference between
`apply`

,`lapply`

,`sapply`

,`tapply`

and`mapply`

.

### Vectorized task automation

Previously we introduced you to `for`

loops: a basic programming construct that is common across many programming languages. R has more optimised way of automating tasks that are not only faster than for loops, but also take away the pain of having to pre-define your results object.

The most common function you will encounter is `lapply`

, and the closely related `sapply`

.

Lets take a look at the following `for`

loop:

```
for (cc in gap[,unique(continent)]) {
popsum <- gap[year == 2007 & continent == cc, sum(pop)]
print(paste(cc, ":", popsum))
}
```

It calculates the total population on each continent, then prints out the result. If instead we want to save these results, we can either make a vector in advance and save the results, or use one of the `apply`

to take care of this detail for us:

```
results <- lapply(gap[,unique(continent)], function(cc) {
popsum <- gap[year == 2007 & continent == cc, sum(pop)]
popsum
})
names(results) <- gap[,unique(continent)]
results
```

```
$Asia
[1] 3811953827
$Europe
[1] 586098529
$Africa
[1] 929539692
$Americas
[1] 898871184
$Oceania
[1] 24549947
```

`lapply`

takes a vector (or list) as its first argument (in this case a vector of the continent names), then a function as its second argument. This function is then executed on every element in the first argument. This is very similar to a for loop: first, `cc`

stores the first continent name, “Asia”, then runs the code in the function body, then `cc`

stores the second continent name, and runs the function body, and so on. The code in the function body can be thought of in exactly the same way as the body of the `for`

loop. The result of the last line is then returned to `lapply`

, which combines the results into a list.

`sapply`

is identical to `lapply`

, except that it tries to simplify the results object. If we run the same code with `sapply`

instead of `lapply`

the results will be returned as a vector:

```
results <- sapply(gap[,unique(continent)], function(cc) {
popsum <- gap[year == 2007 & continent == cc, sum(pop)]
popsum
})
names(results) <- gap[,unique(continent)]
results
```

```
Asia Europe Africa Americas Oceania
3811953827 586098529 929539692 898871184 24549947
```

### apply

The `apply`

function is useful for matrix data: it allows you loop over either the rows or columns of a matrix.

```
# create some dummy data
r <- matrix(rnorm(10*4), nrow=10)
colnames(r) <- letters[1:4]
rownames(r) <- LETTERS[1:10]
```

```
# Get the maximum value in each row:
apply(r, 1, max)
```

```
A B C D E F G
1.9026721 0.8409824 1.2589881 2.3216163 0.9566330 1.3741352 1.8549427
H I J
1.1066072 1.0799746 0.8618778
```

```
# and for each column:
apply(r, 2, max)
```

```
a b c d
1.7335561 1.9026721 0.8618778 2.3216163
```

### mapply

The `mapply`

function can be used to run a function with different combinations of arguments. Let’s take a look at an example:

```
a <- 1:4
b <- 4:1
mapply(rep, a, b)
```

```
[[1]]
[1] 1 1 1 1
[[2]]
[1] 2 2 2
[[3]]
[1] 3 3
[[4]]
[1] 4
```

This is the same as running the following code:

`rep(a[1], b[1])`

```
[1] 1 1 1 1
```

`rep(a[2], b[2])`

```
[1] 2 2 2
```

`rep(a[3], b[3])`

```
[1] 3 3
```

`rep(a[4], b[4])`

```
[1] 4
```

or the following `lapply`

statement:

```
lapply(1:4, function(ii) {
rep(a[ii], b[ii])
})
```

```
[[1]]
[1] 1 1 1 1
[[2]]
[1] 2 2 2
[[3]]
[1] 3 3
[[4]]
[1] 4
```

### tapply

The `tapply`

function allows you to run a function on different groups within a vector. Going back to our first example of the lesson, we can use `tapply`

to calculate the total population for each continent in 2007:

```
gap2007 <- gap[year == 2007] # first filter by the year
tapply(
gap2007[,pop], # The column of population counts from the data frame
gap2007[,continent], # The column of continent labels for each entry
sum # The function to apply to the population vector within each continent
)
```

```
Africa Americas Asia Europe Oceania
929539692 898871184 3811953827 586098529 24549947
```