Most examples for reduce show you how to take an array of numbers and reduce them to a simple sum. Reduce is a great tool for this, but it can do so much more. It is a powerful tool, and essential to functional programming techniques.
Learn how two common array functions - map() and filter() - are syntactic sugar for reduce operations. Learn how to use them, how to compose them, and how using reduce can give you a big performance boost over composing filters and maps over a large data set.
Sometimes we need to turn arrays into new values in ways that can't be done purely by passing an accumulator along with no knowledge about its context. Learn how to reduce an array of numbers into its mathematical mean in a single reduce step by using the optional
array reducer arguments.
Learn a few advanced reduction patterns: flatten allows you to merge a set of arrays into a single array, the dreaded flatmap allows you to convert an array of objects into an array of arrays which then get flattened, and reduceRight allows you to invert the order in which your reducer is applied to your input values.
A common problem when dealing with some kinds of data is that not every object has the same nested structure. lukeskywalker.parents.father.isjedi works, but anakinskywalker.parents.father.isjedi throws an exception, because anakin_skywalker.parents.father is undefined. But we can reduce a path to provide safe default values and avoid exceptions when walking the same path on non-homogenous objects - watch to learn how! :)