# Map, FlatMap, Reduce & More

#### A Simple Problem

released Wed, 20 Feb 2019
Swift Version 5.0

# A simple Problem

Consider this problem: You're getting a list of persons from a JSON endpoint. You'd like to know the average age from all people living in California. The parsed data looks like this:

``````let persons: [[String: Any]] = [[\"name\": \"Carl Saxon\", \"city\": \"New York, NY\", \"age\": 44],

[\"name\": \"Travis Downing\", \"city\": \"El Segundo, CA\", \"age\": 34],

[\"name\": \"Liz Parker\", \"city\": \"San Francisco, CA\", \"age\": 32],

[\"name\": \"John Newden\", \"city\": \"New Jersey, NY\", \"age\": 21],

[\"name\": \"Hector Simons\", \"city\": \"San Diego, CA\", \"age\": 37],

[\"name\": \"Brian Neo\", \"age\": 27]]
``````

Note the last entry, which omits a `city` for the person in question. Those cases have to be silently ignored.

The expected result in the example would be 3 persons , as we have three persons from California. Let's try to implement this in Swift in terms of `compactMap` and `filter`. The `compactMap` is used instead of `map` as `compactMap` automatically ignores empty optionals. So `compactMap([0, nil, 1, 2, nil])` results in `[0, 1, 2]`. This eases the handling of persons without a proper city.

``````func infoFromState(state: String, persons: [[String: Any]])

-> Int {

return persons.compactMap( { \$0[\"city\"]?.componentsSeparatedByString(\", \").last })

.filter({\$0 == state})

.count

}

infoFromState(state: \"CA\", persons: persons)
``````

That's simple enough.

However, now consider the following complication: You'd like to know how many of those persons live in California, and you'd like to know their average age. If we try upgrading the above example, we soon realize that his is a slightly harder problem.

There are various solutions, but they all seem to not fit well with functional constructs. A loop-based approach feels much simpler.

When we think about why this does fit, we realize it is because the shape of the data changes. `map`, `compactMap`, and `filter` all keep the shape of the data similar. Array goes in, array goes out. The amount and the contents of the array may change, but the array-shape stays. The problem above, however, requires us to change the shape to a `struct` or `tuple` with an Integer average and an Integer sum.

These are the kind of problems where you can apply `reduce`. So what is reduce? Lets have a look.