Two of the most fundamental higher-order functions included in the standard library are `map`

and `filter`

. These functions are generic and can operate on any iterable. In particular, they are well-suited for computations on arrays.

Suppose we have a dataset of schools. Each school teaches a particular subject, has a number of classes, and an average number of students per class. We can model a school with the following immutable type:

```
immutable School
subject::Symbol
nclasses::Int
nstudents::Int # average no. of students per class
end
```

Our dataset of schools will be a `Vector{School}`

:

```
dataset = [School(:math, 3, 30), School(:math, 5, 20), School(:science, 10, 5)]
```

Suppose we wish to find the number of students in total enrolled in a math program. To do this, we require several steps:

- we must narrow the dataset down to only schools that teach math (
`filter`

) - we must compute the number of students at each school (
`map`

) - and we must reduce that list of numbers of students to a single value, the sum (
`reduce`

)

A naïve (not most performant) solution would simply be to use those three higher-order functions directly.

```
function nmath(data)
maths = filter(x -> x.subject === :math, data)
students = map(x -> x.nclasses * x.nstudents, maths)
reduce(+, 0, students)
end
```

and we verify there are 190 math students in our dataset:

```
julia> nmath(dataset)
190
```

Functions exist to combine these functions and thus improve performance. For instance, we could have used the `mapreduce`

function to perform the mapping and reduction in one step, which would save time and memory.

The `reduce`

is only meaningful for associative operations like `+`

, but occasionally it is useful to perform a reduction with a non-associative operation. The higher-order functions `foldl`

and `foldr`

are provided to force a particular reduction order.

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