Clojure map, filter, reduce — The Functional Toolkit | Episode 14

Map, filter, and reduce are the three higher-order functions you'll reach for in almost every Clojure program. Once you know them, most data-shaping tasks turn into three-line pipelines. In this episode we cover map to transform each item, filter to keep the ones that match, reduce to fold a collection into a single value, and anonymous functions with fn and the hash-paren shorthand. Phase 3 begins — the Functional Core! Student code: https://github.com/GoCelesteAI/clojure-for-beginners/tree/main/episode14 Every keystroke is shown on screen with generous pauses so you can follow along at your own pace. What You'll Learn: - map — apply a function to every element in a collection - filter — keep elements that satisfy a predicate - reduce — fold a collection into a single value, with optional seed - Anonymous functions — (fn [x] ...) and the #(...) shorthand - Building pipelines by combining map, filter, and reduce Timestamps: 0:00 - Intro 0:12 - Preview: map, filter, reduce 0:32 - Start the REPL 0:40 - map in the REPL 0:53 - filter in the REPL 1:07 - reduce in the REPL 1:23 - Exit REPL 1:28 - Write mapfilterreduce.clj in Neovim 1:51 - map section 3:11 - filter section 4:17 - reduce section 5:36 - Orders pipeline: filter, map, reduce 7:56 - Run with :!clj -M % 7:59 - Output walked through 8:55 - Review 9:00 - Recap 9:35 - What's next: Episode 15 Key Takeaways: 1. map transforms each item. filter keeps the matches. reduce folds to one value. 2. Anonymous functions let you pass tiny lambdas inline — use #() for one-off cases. 3. Combine map, filter, and reduce to build readable data pipelines. 4. These three functions cover 90 percent of everyday data processing in Clojure. Phase 3 begins! Next up, Episode 15 — threading macros for even cleaner pipelines. Taught by CelesteAI. Like and subscribe for more Clojure tutorials!