6.9 Exercises: Implementing Complex Data Flows

Mastering Higher-Order Functions with Complex Data Flow Exercises

In this section, you’ll apply your understanding of higher-order functions to solve sophisticated data flow problems. These exercises are crafted to challenge your problem-solving skills and solidify your comprehension of key functional programming concepts in Clojure.

Exercise 1: Data Pipeline with Multiple Transformations

The goal of this exercise is to construct a data pipeline that processes a list of customer transactions. You’ll use higher-order functions to apply a series of transformations efficiently.

Problem Statement:

You have a list of transactions. Each transaction is a map containing details: {:customer-id 123, :amount 100, :status :pending}.

1. Filter transactions with status :pending.
2. Apply a discount of 10% to the :amount for each pending transaction.
3. Calculate the total sum of discounted transactions.

Implementation:

Start by writing higher-order functions like map, filter, and reduce to achieve the desired transformation:

(def transactions
  [{:customer-id 123, :amount 100, :status :pending}
   {:customer-id 456, :amount 250, :status :complete}
   {:customer-id 789, :amount 150, :status :pending}])

(defn process-transactions [transactions]
  (->> transactions
       (filter #(= (:status %) :pending))
       (map #(update % :amount * 0.9))
       (reduce + (map :amount))))

(println (process-transactions transactions)) ; Output: Updated total sum

Exercise 2: Function Generators

Create a function that returns another function, generating logic based on input parameters.

Problem Statement:

Define a function create-multiplier that, given a number n, returns another function that multiplies its input by n.

Implementation:

(defn create-multiplier [n]
  (fn [x]
    (* n x)))

(def multiply-by-3 (create-multiplier 3))

(println (multiply-by-3 10)) ; Output: 30

Exercise 3: Custom Iterators

Develop a function that mimics iteration over a data structure but tailors logic to specific needs.

Problem Statement:

Write a function custom-iter that takes a collection and applies a specific action to each item. For this exercise, make a setup that records logs for any improvement on values.

Implementing this requires considering additional logging or operations beyond standard iteration:

(defn log-improvements [coll]
  (doseq [item coll]
    (when (> item 5)
      (println (str "Logged improvement: " item)))))

(def data [2 3 6 8 1 7])
(log-improvements data)

Practice Quiz

By tackling these exercises, you hone the skills essential to master higher-order functions in Clojure, facilitating complex data flows, adaptable function logic, and efficient data iteration.