Welcome to today's lesson on applying Kotlin to perform data filtering and aggregation in a real-world scenario using a user management system. We'll start by building a foundational structure that can handle basic user operations. Then, we'll expand it by introducing more advanced functionalities that allow filtering and aggregating user data.

In our starter task, we will implement a class that manages basic operations on a collection of user data, specifically handling the addition of new users, retrieving user profiles, and updating user profiles.

Here are the starter task methods:

• addUser(userId: String, age: Int, country: String, subscribed: Boolean): Boolean - adds a new user with the specified attributes. Returns true if the user was added successfully and false if a user with the same userId already exists.

• getUser(userId: String): Map<String, Any?>? - returns the user's profile as a map if the user exists; otherwise, returns null.

• updateUser(userId: String, age: Int?, country: String?, subscribed: Boolean?): Boolean - updates the user's profile based on non-null parameters. Returns true if the user exists and was updated, false otherwise.

Here is the implementation of our starter task using Kotlin:

This implementation covers all our starter methods. Let's move forward and introduce more complex functionalities.

With our foundational structure in place, it's time to add functionalities for filtering user data and aggregating statistics.

Here are new methods to implement in Kotlin:

• filterUsers(minAge: Int?, maxAge: Int?, country: String?, subscribed: Boolean?): List<String>:

  • Returns the list of user IDs that match the specified criteria. Criteria can be null, meaning that criterion should not be applied during filtering.

• aggregateStats(): Map<String, Number> - returns statistics in the form of a map:

  • total_users: Total number of users
  • average_age: Average age of all users (rounded down to the nearest integer)
  • subscribed_ratio: Ratio of subscribed users to total users (as a double with two decimals)

This method filters users based on the criteria provided. Let's see how it works in Kotlin:

The filterUsers method filters users based on minAge, maxAge, country, and subscribed status criteria. It uses Kotlin's filter function on the users map, where (_, profile) destructures each map entry - the underscore _ discards the userId (key) since it's not needed for filtering, and profile represents the user's data map. The method uses null-safe conditions (like minAge == null || profile["age"] as Int >= minAge) to only apply filters when criteria are provided. Users meeting all specified criteria are included in the result, and their IDs are returned as a list using .keys.toList(). The example usage shows filtering scenarios like finding users from USA who are subscribed and between ages 20-30, which returns matching user IDs.

This method aggregates statistics from the user profiles. Let's implement it in Kotlin:

This aggregateStats method calculates and returns aggregate statistics about the users in the form of a map. It first determines totalUsers, the total number of users. If there are no users, it returns a map with zeroed statistics. Otherwise, it calculates totalAge by summing the ages of all users and counts subscribedUsers who are subscribed. It then computes averageAge via integer division of totalAge by totalUsers and calculates subscribedRatio by dividing subscribedUsers by totalUsers and rounding to two decimal places. The resulting statistics map includes total_users, average_age, and subscribed_ratio.

Here's the complete UserManager class with all methods, including the new ones for filtering and aggregation:

Great job! Today, you've learned how to effectively handle user data by implementing advanced functionalities like filtering and aggregation on top of a basic system. This is a critical skill in real-life software development, where you often need to extend existing systems to meet new requirements.

I encourage you to practice solving similar challenges to solidify your understanding of data filtering and aggregation. Happy coding, and see you in the next lesson!