Lectures

We break down how to work with collection grouping in Kotlin via groupBy: why the result is Map<K, List<T>>, how to read groups safely (iteration and lookup by key with ?:), how to normalize keys ( trim()/ lowercase()), and how to use the overload groupBy(keySelector, valueTransform) to store only the data you actually need inside the groups.

We break down how to count frequencies in Kotlin (how many times a key occurs) without extra lists: how groupBy differs from groupingBy, why groupingBy returns not a Map but a “grouping plan,” and how a single operation eachCount() produces a ready-made frequency map Map<K, Int>. In practice, we count the number of expenses by category while normalizing the key via trim() and lowercase(), and we review typical mistakes.

We cover the typical “last mile” when working with grouping results in Kotlin: how to turn groupBy into a summary via mapValues, when it’s better to use groupingBy().eachCount(), how to sort maps via entries (by key and by value), select top‑N with take, and then assemble a human-readable report using joinToString and StringBuilder with column alignment ( padEnd/ padStart). At the end, there’s a set of common mistakes and how to avoid them.

We build a mini text analyzer in Kotlin: we assemble a pipeline “normalization → tokenization → frequencies → sorting/output”, carefully bring keys to a uniform form via trim() and lowercase(), count frequencies in one line with groupingBy + eachCount(), and then print the Top‑N with descending sorting.

We break down two basic “result formats” for collection-based reports: detailed output via groupBy ( Map key → List) and a summary via groupingBy + eachCount() ( Map key → number). We learn to normalize keys ( trim(), lowercase()), format output with StringBuilder, sort by entries, and connect reports to CLI commands.