Maps, sets, and occurrence policies

The idea

A map associates keys with values, while a set records membership without an additional value. For counting, read an existing total or zero, then store the updated total. A missing lookup and a stored zero can produce the same fallback result, so use containsKey when presence itself matters. Decide whether zero totals remain meaningful entries before removing anything from an aggregation.

A first-occurrence deduplicator combines a set of normalized keys with a result list. Add the original display value only when its key is seen for the first time. This preserves the input’s display order without relying on a particular map implementation’s traversal order. Normalization and deduplication are separate requirements: case-insensitive comparison should not automatically destroy the spelling that the interface intends to show.

fun main(){ val counts=mutableMapOf<String,Int>(); for(word in listOf("red","blue","red")) counts[word]=(counts[word]?:0)+1; println(counts["red"]); println(counts.containsKey("green")) }

2
false

Repeated red observations update one key, while a lookup policy does not insert unrelated absent keys.